library(tidyverse)
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library(MSstatsPTM)
library(lme4)
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library(msqrob2)
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Load in all data

directory = "C:/Users/Nina/OneDrive - UGent/Documenten/Doctoraat/msqrobPTM paper/biological ubiquitin dataset/"
#MSStats data
load(paste0(directory, "MSstatsSummarizedData.RData"))
load(paste0(directory, "MSstatsModel.rda"))
#Msqrob
load("pe_ubiquitin_msqrob.rda")

1 MSstats

estimated ptm abundance ->fixef(model_ptm\(Model.Details\)PTM[[100]]) protein abundance (for protein i) -> summarized_ptm\(PROTEIN\)ProteinLevelData %>% filter(Protein == i) %>% pull(LogIntensities) ptm abundance (for ptm i) -> summarized_ptm\(PTM\)ProteinLevelData %>% filter(Protein == i) %>% pull(LogIntensities) normalised ptm abundance -> model_ptm\(Model.Details\)PTM[[100]] - model_ptm\(Model.Details\)PROTEIN[[100]]

At the moment, only in the GitHub version, a detailed model gets outputted with info on the estimates on everything, so I used that here

originalRUN <- c("CCCP-B1T1", "CCCP-B1T2", "CCCP-B2T1", "CCCP-B2T2", "Combo-B1T1", 
                 "Combo-B1T2", "Combo-B2T1", "Combo-B2T2", "Ctrl-B1T1", "Ctrl-B1T2",   
                 "Ctrl-B2T1", "Ctrl-B2T2", "USP30_OE-B1T1", "USP30_OE-B1T2", 
                 "USP30_OE-B2T1", "USP30_OE-B2T2")
GROUP <- sapply(strsplit(originalRUN, "-"), function(x) x[1])
## Plot fourty ptm's from Combo vs Ctrl
ptm_list1 <- model_ptm$ADJUSTED.Model %>% filter(Label == "Combo vs Ctrl") %>% filter(adj.pvalue <= 0.05) %>% head(40) %>% pull(Protein)
all_ptms <- model_ptm$PTM.Model %>% pull(Protein) %>% unique()
all_proteins <- model_ptm$PROTEIN.Model %>% pull(Protein) %>% unique()
adjusted_ptms <- model_ptm$ADJUSTED.Model %>% pull(Protein) %>% unique()
plots <- c()
for (i in ptm_list1){
  print(i)
  prot <- strsplit(i, "_")[[1]][1]
  index <- which(all_ptms == i)
  index_prot <- which(all_proteins == prot)
  index_adjusted <- which(adjusted_ptms == i)
#Protein abundance
protein <- tibble(originalRUN, GROUP)
protein$FeatureType <- "Protein"
protein$Protein <- prot
tmp <- summarized_ptm$PROTEIN$ProteinLevelData %>% filter(Protein == prot) %>%
  select(c(LogIntensities, originalRUN))
missing_runs <- setdiff(originalRUN, tmp$originalRUN)
tmp2 <- tibble(originalRUN = missing_runs, LogIntensities = NA)
tmp3 <- rbind(tmp, tmp2) %>% arrange(originalRUN) %>% select(LogIntensities)
protein <- cbind(protein, tmp3)

#PTM abundance
ptm_df <- summarized_ptm$PTM$ProteinLevelData %>% filter(Protein == i)
ptm_df$Abundance <- ptm_df$LogIntensities
ptm_df$FeatureType <- "PTM"
ptm_df <- ptm_df %>% select(c("Protein", "LogIntensities", "originalRUN", 
                                "GROUP", "FeatureType"))

#PTM estimated (maar nog niet adjusted)
Protein <- rep(i, length(originalRUN))
ptm_estimate <- tibble(Protein)
ptm_estimate$originalRUN <- originalRUN
ptm_estimate$GROUP <- GROUP
ptm_estimate$FeatureType <- "PTM_estimated"
ptm_estimate$LogIntensities <- NA

#Check that model estimates are definitely from that PTM
if (class(model_ptm$Model.Details$PTM[[index]]) == "lm"){print(i)
  }else{
print(all(
  summarized_ptm$PTM$ProteinLevelData %>% filter(Protein == i) %>% pull(LogIntensities) ==
    getME(model_ptm$Model.Details$PTM[[index]], "y")
))}

if (class(model_ptm$Model.Details$PTM[[index]]) == "lm"){
fixeffects <- model_ptm$Model.Details$PTM[[index]]$coefficients
} else {fixeffects <- fixef(model_ptm$Model.Details$PTM[[index]])} 
ptm_estimate[ptm_estimate$GROUP=="CCCP",]$LogIntensities <- 
                                                    fixeffects[["(Intercept)"]]
try(ptm_estimate[ptm_estimate$GROUP=="Combo",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("Combo", names(fixeffects))])
try(ptm_estimate[ptm_estimate$GROUP=="Ctrl",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("Ctrl", names(fixeffects))])
try(ptm_estimate[ptm_estimate$GROUP=="USP30_OE",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("USP30_OE", names(fixeffects))])

#Protein estimated (maar nog niet adjusted)
Protein <- rep(prot, length(originalRUN))
prot_estimate <- tibble(Protein)
prot_estimate$originalRUN <- originalRUN
prot_estimate$GROUP <- GROUP
prot_estimate$FeatureType <- "Protein_estimated"
prot_estimate$LogIntensities <- NA

#Check that model estimates are definitely from that PTM
if (class(model_ptm$Model.Details$PROTEIN[[index_prot]]) == "lmerMod"){
  print(all(
  summarized_ptm$PROTEIN$ProteinLevelData %>% filter(Protein == prot) %>% pull(LogIntensities) ==
    getME(model_ptm$Model.Details$PROTEIN[[index_prot]], "y")))
}

if (class(model_ptm$Model.Details$PROTEIN[[index_prot]]) == "lm"){
fixeffects <- model_ptm$Model.Details$PROTEIN[[index_prot]]$coefficients
} else {fixeffects <- fixef(model_ptm$Model.Details$PROTEIN[[index_prot]])} 
prot_estimate[prot_estimate$GROUP=="CCCP",]$LogIntensities <- 
                                                    fixeffects[["(Intercept)"]]
try(prot_estimate[prot_estimate$GROUP=="Combo",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("Combo", names(fixeffects))])
try(prot_estimate[prot_estimate$GROUP=="Ctrl",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("Ctrl", names(fixeffects))])
try(prot_estimate[prot_estimate$GROUP=="USP30_OE",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("USP30_OE", names(fixeffects))])

#PTM normalised
Protein <- rep(i, length(originalRUN))
ptm_norm <- tibble(Protein)
ptm_norm$originalRUN <- originalRUN
ptm_norm$GROUP <- GROUP
ptm_norm$FeatureType <- "PTM_normalised"
ptm_norm$LogIntensities <- ptm_estimate$LogIntensities - prot_estimate$LogIntensities


plot_df <- rbindlist(list(protein, ptm_df, ptm_estimate, prot_estimate, ptm_norm), fill = TRUE)
plot_points <- rbindlist(list(protein, ptm_df), fill = TRUE)

#plot_df[plot_df$FeatureType == 'Model'][['FeatureType']] <- "PTM Summarized"
#plot_df[plot_df$FeatureType == 'Peptide'][['FeatureType']] <- "PTM Feature"

p1 <- plot_df %>% ggplot() +
  geom_line(aes(x = originalRUN, y = LogIntensities , group = FeatureType, color = FeatureType), size =2) +
  geom_point(data = plot_points, aes(x = originalRUN, y = LogIntensities , 
                                     group = FeatureType, color = FeatureType), size = 5) +
  geom_vline(data=data.frame(x = c(4.5, 8.5, 12.5)),
             aes(xintercept=as.numeric(x)), linetype = "dashed") +
    scale_colour_manual(values = c("PTM" = "palevioletred1", "PTM_estimated" = "deeppink4", 
                                   "Protein" = "palegreen2", 
                                 "Protein_estimated" = "seagreen4",
                                 "PTM_normalised" = "goldenrod2")) +
  #scale_size_manual(values = c(1, 2)) +
  labs(title = i, x = "BioReplicate", y = "LogIntensity") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 60, hjust=1, size = 16),
        axis.text.y = element_text(size = 16),
        legend.text=element_text(size=11.5),
        axis.title.y = element_text(size = 22),
        axis.title.x = element_text(size = 22),
        title = element_text(size = 22),
        strip.text = element_text(size = 16),
        legend.title =  element_blank(),
        legend.direction = "horizontal",
        legend.position = c(0.5, 0.05)) +
  annotate("text", x = 2.5, y = 30, label = "CCCP", size = 8) +
  annotate("text", x = 6.5, y = 30, label = "Combo", size = 8) +
  annotate("text", x = 10.5, y = 30, label = "Ctrl", size = 8) +
  annotate("text", x = 14.5, y = 30, label = "USP30", size = 8) +
  ylim(-6, 30)

plots[[i]] <- p1
}
## [1] "A0FGR9_K608"
## [1] "A0FGR9_K608"
## [1] "B2RB47_K073"
## [1] "B2RB47_K073"
## [1] "O00154_K205"
## [1] "O00154_K205"
## [1] TRUE
## [1] "O00159_K0578"
## [1] TRUE
## [1] TRUE
## [1] "O00170_K215"
## [1] "O00170_K215"
## [1] TRUE
## [1] "O00232_K262"
## [1] "O00232_K262"
## [1] TRUE
## [1] "O00267_K0627"
## [1] "O00267_K0627"
## [1] TRUE
## [1] "O00410_K0806"
## [1] TRUE
## [1] TRUE
## [1] "O14972_K103"
## [1] TRUE
## [1] "O14980_K0686"
## [1] "O14980_K0686"
## [1] TRUE
## [1] "O15372_K274"
## [1] "O15372_K274"
## [1] TRUE
## [1] "O43175_K289"
## [1] TRUE
## [1] TRUE
## [1] "O43670_K100"
## [1] TRUE
## [1] "O43847_K0993"
## [1] "O43847_K0993"
## [1] "O60260_K076"
## [1] TRUE
## [1] TRUE
## [1] "O60260_K220"
## [1] TRUE
## [1] TRUE
## [1] "O60260_K369"
## [1] TRUE
## [1] TRUE
## [1] "O60260_K408"
## [1] TRUE
## [1] TRUE
## [1] "O60260_K435"
## [1] "O60260_K435"
## [1] TRUE
## [1] "O60341_K492"
## [1] "O60341_K492"
## [1] "O60361_K034"
## [1] "O60361_K034"
## [1] TRUE
## [1] "O60361_K041"
## [1] TRUE
## [1] TRUE
## [1] "O60361_K070"
## [1] "O60361_K070"
## [1] TRUE
## [1] "O60361_K109"
## [1] TRUE
## [1] TRUE
## [1] "O60361_K113"
## [1] "O60361_K113"
## Error : Assigned data `fixeffects[["(Intercept)"]] + ...` must be compatible with existing data.
## ✖ Existing data has 4 rows.
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## ℹ Only vectors of size 1 are recycled.
## [1] TRUE
## [1] "O60506_K336"
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## [1] "O60749_K218"
## [1] TRUE
## [1] "O60749_K516"
## [1] "O60749_K516"
## [1] "O60832_K433"
## [1] "O60832_K433"
## [1] TRUE
## [1] "O60888_K082"
## [1] "O60888_K082"
## [1] TRUE
## [1] "O75131_K158"
## [1] "O75131_K158"
## Error : Assigned data `fixeffects[["(Intercept)"]] + ...` must be compatible with existing data.
## ✖ Existing data has 4 rows.
## ✖ Assigned data has 0 rows.
## ℹ Only vectors of size 1 are recycled.
## [1] "O75150_K440"
## [1] "O75150_K440"
## Error : Assigned data `fixeffects[["(Intercept)"]] + ...` must be compatible with existing data.
## ✖ Existing data has 4 rows.
## ✖ Assigned data has 0 rows.
## ℹ Only vectors of size 1 are recycled.
## [1] "O75400_K669"
## [1] "O75400_K669"
## [1] TRUE
## [1] "O75694_K0740"
## [1] TRUE
## [1] "O75694_K0824"
## [1] "O75694_K0824"
## [1] "O75694_K0890"
## [1] "O75694_K0890"
## [1] "O75694_K0902"
## [1] TRUE
## [1] "O75694_K0987"
## [1] TRUE
## [1] "O75694_K1069"
## [1] "O75694_K1069"
## [1] "O75694_K1126"
## [1] TRUE
plots
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model_ptm$ADJUSTED.Model %>% filter(Protein == "O60260_K369", Label == "Combo vs Ctrl")

2 Msqrob

estimated ptm abundance -> rowData(pe[[“ptmRel”]])\(msqrobModels\)P18124_K029 %>% getCoef() protein abundance -> assay(pe[[“proteinRobust”]]) ptm abundance -> this I do not have, is already normalised normalised ptm abundance -> assay(pe[[“ptmRel”]])

## Plot ptm's from Combo vs Ctrl
ptm_list2 <- rowData(pe[["ptmRel"]])$groupCombo %>% filter(adjPval <= 0.05) %>% rownames()
plots <- c()
for (i in ptm_list2){
  print(i)
  prot <- strsplit(i, "_")[[1]][1]

#Protein abundance
protein <- assay(pe[["proteinRaw"]])[prot,] %>% as.data.frame() 
colnames(protein) <- "LogIntensities"
protein <- protein %>%
  mutate(Protein = prot,
         FeatureType = "Protein",
         GROUP = sapply(strsplit(rownames(protein), "-"), function(x) x[1])) %>%
         rownames_to_column("originalRUN") %>%
         arrange(GROUP, originalRUN)

#Peptidoform abundance
pepforms <- rowData(pe[["peptidoformRaw"]]) %>% as.data.frame() %>% 
    filter(ptm == i) %>% rownames
pepform <- assay(pe[["peptidoformRaw"]])[pepforms,] %>% as.data.frame()
if (length(pepforms)==0) {next}
if (length(pepforms)>1){
  pepform <- pepform %>% rownames_to_column("Protein") %>% 
    pivot_longer(cols = colnames(pepform), names_to = "originalRUN",
                 values_to = "LogIntensities") 
  pepform$GROUP <- sapply(strsplit(pepform$originalRUN, "-"), function(x) x[1])
  pepform <- pepform %>% arrange(GROUP, originalRUN)
} else {colnames(pepform) <- "LogIntensities"
        pepform <- pepform %>% rownames_to_column("originalRUN") 
        pepform <- pepform %>% mutate(GROUP = 
                    sapply(strsplit(pepform$originalRUN, "-"), function(x) x[1]),
                    Protein = pepforms[1]) %>%
                   arrange(GROUP, originalRUN)
        }
pepform <- pepform %>%
  mutate(FeatureType = "Peptidoform")
#pepform <- select(-c("pepform"))
pepform$originalRUN <- forcats::fct_inorder(pepform$originalRUN)

#Peptidoform abundance - normalised
pepforms <- rowData(pe[["pepformRel"]]) %>% as.data.frame() %>% 
    filter(ptm == i) %>% rownames
pepform_norm <- assay(pe[["pepformRel"]])[pepforms,] %>% as.data.frame()
if (length(pepforms)>1){
  pepform_norm <- pepform_norm %>% rownames_to_column("Protein") %>% 
    pivot_longer(cols = colnames(pepform_norm), names_to = "originalRUN",
                 values_to = "LogIntensities") 
  pepform_norm$GROUP <- sapply(strsplit(pepform_norm$originalRUN, "-"), function(x) x[1])
  pepform_norm <- pepform_norm %>% arrange(GROUP, originalRUN) %>%
    mutate(Protein = paste(Protein,"norm"))
} else {colnames(pepform_norm) <- "LogIntensities"
        pepform_norm <- pepform_norm %>% rownames_to_column("originalRUN") 
        pepform_norm <- pepform_norm %>% mutate(GROUP = 
                    sapply(strsplit(pepform_norm$originalRUN, "-"), function(x) x[1]),
                    Protein = paste(pepforms[1],"norm")) %>%
                   arrange(GROUP, originalRUN)
        }
pepform_norm <- pepform_norm %>%
  mutate(FeatureType = "Peptidoform - normalised")
#pepform <- select(-c("pepform"))
pepform_norm$originalRUN <- forcats::fct_inorder(pepform_norm$originalRUN)

#Normalised PTM abundance
ptm_df <- assay(pe[["ptmRel"]])[i,] %>% as.data.frame()
colnames(ptm_df) <- "LogIntensities"
ptm_df <- ptm_df %>%
  mutate(Protein = i,
         FeatureType = "PTM - normalised",
         GROUP = sapply(strsplit(rownames(ptm_df), "-"), function(x) x[1])) %>%
         rownames_to_column("originalRUN") %>%
         arrange(GROUP, originalRUN)


#PTM estimated
Protein <- paste(rep(i, nrow(ptm_df)), "estimate")
ptm_estimate <- tibble(Protein)
ptm_estimate$originalRUN <- ptm_df$originalRUN
ptm_estimate$GROUP <- ptm_df$GROUP
ptm_estimate$FeatureType <- "PTM_estimated"
ptm_estimate$LogIntensities <- NA

fixeffects <- rowData(pe[["ptmRel"]])$msqrobModels[[i]] %>% getCoef
ptm_estimate[ptm_estimate$GROUP=="Ctrl",]$LogIntensities <- 
                                                    fixeffects[["(Intercept)"]]
ptm_estimate[ptm_estimate$GROUP=="Combo",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("Combo", names(fixeffects))][1]
ptm_estimate[ptm_estimate$GROUP=="CCCP",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("CCCP", names(fixeffects))][1]
ptm_estimate[ptm_estimate$GROUP=="USP30_OE",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("USP30_OE", names(fixeffects))][1]
ptm_estimate <- ptm_estimate %>% arrange(GROUP, originalRUN)

plot_df <- rbindlist(list(protein, pepform, pepform_norm, ptm_df, ptm_estimate), fill = TRUE)
plot_df$originalRUN <- forcats::fct_inorder(plot_df$originalRUN)
plot_points <- plot_df %>% filter(FeatureType != "PTM_estimated")

#plot_df[plot_df$FeatureType == 'Model'][['FeatureType']] <- "PTM Summarized"
#plot_df[plot_df$FeatureType == 'Peptide'][['FeatureType']] <- "PTM Feature"

p1 <- plot_df %>% ggplot() +
  geom_line(aes(x = originalRUN, y = LogIntensities , group = Protein, color = FeatureType), size =2) +
  geom_point(data = plot_points, aes(x = originalRUN, y = LogIntensities , group = Protein, color = FeatureType), size = 5) +
  geom_vline(data=data.frame(x = c(4.5, 8.5, 12.5)),
             aes(xintercept=as.numeric(x)), linetype = "dashed") +
  scale_colour_manual(values = c("Peptidoform - normalised" = "#C3C3C3", 
                                 "Protein" = "dodgerblue2", "PTM - normalised" = "seagreen3", 
                                 "PTM_estimated" = "palevioletred2", "Peptidoform" = "gray36"))  +
  #scale_size_manual(values = c(1, 2)) +
  labs(title = i, x = "BioReplicate", y = "LogIntensity") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 60, hjust=1, size = 16),
        axis.text.y = element_text(size = 16),
        legend.text=element_text(size=10),
        axis.title.y = element_text(size = 22),
        axis.title.x = element_text(size = 22),
        title = element_text(size = 22),
        strip.text = element_text(size = 16),
        legend.title =  element_blank(),
        legend.direction = "horizontal",
        legend.position = c(0.5, 0.05)) +
  annotate("text", x = 2.5, y = 30, label = "CCCP", size = 8) +
  annotate("text", x = 6.5, y = 30, label = "Combo", size = 8) +
  annotate("text", x = 10.5, y = 30, label = "Ctrl", size = 8) +
  annotate("text", x = 14.5, y = 30, label = "USP30", size = 8) +
  ylim(-5, 30)

plots[[i]] <- p1
}
## [1] "O43242_K273"
## [1] "O94826_K168"
## [1] "O94826_K300"
## [1] "O94826_K078"
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## [1] "P21333_K0906"
## [1] "P36578_K106"
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## [1] "P54920_K227"
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## [1] "P60174_K051"
## [1] "P61247_K056"
## [1] "P61978_K052"
## [1] "Q00610_K0911"
## [1] "Q13332_K1685"
## [1] "Q13485_K070"
## [1] "Q14566_K611"
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## [1] "Q99613_K712"
## [1] "Q9NZ45_K089"
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## [1] "Q9Y490_K0334"
plots
## $O43242_K273

## 
## $O94826_K168
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## $P05023_K0671
## Warning: Removed 4 row(s) containing missing values (geom_path).
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## $P05023_K0727
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## $P14618_K207
## Warning: Removed 14 row(s) containing missing values (geom_path).
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## $P19367_K501
## Warning: Removed 2 row(s) containing missing values (geom_path).
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## $P21333_K0906
## Warning: Removed 6 rows containing missing values (geom_point).

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## Warning: Removed 2 rows containing missing values (geom_point).

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## $P46777_K027

## 
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## $P52789_K041
## Warning: Removed 3 rows containing missing values (geom_point).

## 
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## 
## $P56937_K321
## Warning: Removed 23 row(s) containing missing values (geom_path).
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## $P60174_K051
## Warning: Removed 3 rows containing missing values (geom_point).

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## $P61247_K056
## Warning: Removed 4 row(s) containing missing values (geom_path).
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## 
## $P61978_K052

## 
## $Q00610_K0911

## 
## $Q13332_K1685
## Warning: Removed 3 rows containing missing values (geom_point).

## 
## $Q13485_K070

## 
## $Q14566_K611

## 
## $Q15154_K0970
## Warning: Removed 14 row(s) containing missing values (geom_path).
## Warning: Removed 22 rows containing missing values (geom_point).

## 
## $Q8WXW3_K708
## Warning: Removed 3 rows containing missing values (geom_point).

## 
## $Q969Z3_K294
## Warning: Removed 40 row(s) containing missing values (geom_path).
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## 
## $Q99613_K712

## 
## $Q9NZ45_K089
## Warning: Removed 70 row(s) containing missing values (geom_path).
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## $Q9UHP3_K0201
## Warning: Removed 24 row(s) containing missing values (geom_path).
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## 
## $Q9Y490_K0334
## Warning: Removed 2 row(s) containing missing values (geom_path).
## Warning: Removed 4 rows containing missing values (geom_point).

2.0.1 CCCP vs Ctrl

## Plot ptm's from CCCP vs Ctrl
ptm_listCCCP <- rowData(pe[["ptmRel"]])$groupCCCP %>% filter(adjPval <= 0.05) %>% rownames()
plots <- c()
for (i in ptm_listCCCP){
  print(i)
  prot <- strsplit(i, "_")[[1]][1]

#Protein abundance
protein <- assay(pe[["proteinRaw"]])[prot,] %>% as.data.frame() 
colnames(protein) <- "LogIntensities"
protein <- protein %>%
  mutate(Protein = prot,
         FeatureType = "Protein",
         GROUP = sapply(strsplit(rownames(protein), "-"), function(x) x[1])) %>%
         rownames_to_column("originalRUN") %>%
         arrange(GROUP, originalRUN)

#Peptidoform abundance
pepforms <- rowData(pe[["peptidoformRaw"]]) %>% as.data.frame() %>% 
    filter(ptm == i) %>% rownames
pepform <- assay(pe[["peptidoformRaw"]])[pepforms,] %>% as.data.frame()
if (length(pepforms)==0) {next}
if (length(pepforms)>1){
  pepform <- pepform %>% rownames_to_column("Protein") %>% 
    pivot_longer(cols = colnames(pepform), names_to = "originalRUN",
                 values_to = "LogIntensities") 
  pepform$GROUP <- sapply(strsplit(pepform$originalRUN, "-"), function(x) x[1])
  pepform <- pepform %>% arrange(GROUP, originalRUN)
} else {colnames(pepform) <- "LogIntensities"
        pepform <- pepform %>% rownames_to_column("originalRUN") 
        pepform <- pepform %>% mutate(GROUP = 
                    sapply(strsplit(pepform$originalRUN, "-"), function(x) x[1]),
                    Protein = pepforms[1]) %>%
                   arrange(GROUP, originalRUN)
        }
pepform <- pepform %>%
  mutate(FeatureType = "Peptidoform")
#pepform <- select(-c("pepform"))
pepform$originalRUN <- forcats::fct_inorder(pepform$originalRUN)

#Peptidoform abundance - normalised
pepforms <- rowData(pe[["pepformRel"]]) %>% as.data.frame() %>% 
    filter(ptm == i) %>% rownames
pepform_norm <- assay(pe[["pepformRel"]])[pepforms,] %>% as.data.frame()
if (length(pepforms)>1){
  pepform_norm <- pepform_norm %>% rownames_to_column("Protein") %>% 
    pivot_longer(cols = colnames(pepform_norm), names_to = "originalRUN",
                 values_to = "LogIntensities") 
  pepform_norm$GROUP <- sapply(strsplit(pepform_norm$originalRUN, "-"), function(x) x[1])
  pepform_norm <- pepform_norm %>% arrange(GROUP, originalRUN) %>%
    mutate(Protein = paste(Protein,"norm"))
} else {colnames(pepform_norm) <- "LogIntensities"
        pepform_norm <- pepform_norm %>% rownames_to_column("originalRUN") 
        pepform_norm <- pepform_norm %>% mutate(GROUP = 
                    sapply(strsplit(pepform_norm$originalRUN, "-"), function(x) x[1]),
                    Protein = paste(pepforms[1],"norm")) %>%
                   arrange(GROUP, originalRUN)
        }
pepform_norm <- pepform_norm %>%
  mutate(FeatureType = "Peptidoform - normalised")
#pepform <- select(-c("pepform"))
pepform_norm$originalRUN <- forcats::fct_inorder(pepform_norm$originalRUN)

#Normalised PTM abundance
ptm_df <- assay(pe[["ptmRel"]])[i,] %>% as.data.frame()
colnames(ptm_df) <- "LogIntensities"
ptm_df <- ptm_df %>%
  mutate(Protein = i,
         FeatureType = "PTM - normalised",
         GROUP = sapply(strsplit(rownames(ptm_df), "-"), function(x) x[1])) %>%
         rownames_to_column("originalRUN") %>%
         arrange(GROUP, originalRUN)


#PTM estimated
Protein <- paste(rep(i, nrow(ptm_df)), "estimate")
ptm_estimate <- tibble(Protein)
ptm_estimate$originalRUN <- ptm_df$originalRUN
ptm_estimate$GROUP <- ptm_df$GROUP
ptm_estimate$FeatureType <- "PTM_estimated"
ptm_estimate$LogIntensities <- NA

fixeffects <- rowData(pe[["ptmRel"]])$msqrobModels[[i]] %>% getCoef
ptm_estimate[ptm_estimate$GROUP=="Ctrl",]$LogIntensities <- 
                                                    fixeffects[["(Intercept)"]]
ptm_estimate[ptm_estimate$GROUP=="Combo",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("Combo", names(fixeffects))][1]
ptm_estimate[ptm_estimate$GROUP=="CCCP",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("CCCP", names(fixeffects))][1]
ptm_estimate[ptm_estimate$GROUP=="USP30_OE",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("USP30_OE", names(fixeffects))][1]
ptm_estimate <- ptm_estimate %>% arrange(GROUP, originalRUN)

plot_df <- rbindlist(list(protein, pepform, pepform_norm, ptm_df, ptm_estimate), fill = TRUE)
plot_df$originalRUN <- forcats::fct_inorder(plot_df$originalRUN)
plot_points <- plot_df %>% filter(FeatureType != "PTM_estimated")

#plot_df[plot_df$FeatureType == 'Model'][['FeatureType']] <- "PTM Summarized"
#plot_df[plot_df$FeatureType == 'Peptide'][['FeatureType']] <- "PTM Feature"

p1 <- plot_df %>% ggplot() +
  geom_line(aes(x = originalRUN, y = LogIntensities , group = Protein, color = FeatureType), size =2) +
  geom_point(data = plot_points, aes(x = originalRUN, y = LogIntensities , group = Protein, color = FeatureType), size = 5) +
  geom_vline(data=data.frame(x = c(4.5, 8.5, 12.5)),
             aes(xintercept=as.numeric(x)), linetype = "dashed") +
  scale_colour_manual(values = c("Peptidoform - normalised" = "#C3C3C3", 
                                 "Protein" = "dodgerblue2", "PTM - normalised" = "seagreen3", 
                                 "PTM_estimated" = "palevioletred2", "Peptidoform" = "gray36"))  +
  #scale_size_manual(values = c(1, 2)) +
  labs(title = i, x = "BioReplicate", y = "LogIntensity") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 60, hjust=1, size = 16),
        axis.text.y = element_text(size = 16),
        legend.text=element_text(size=10),
        axis.title.y = element_text(size = 22),
        axis.title.x = element_text(size = 22),
        title = element_text(size = 22),
        strip.text = element_text(size = 16),
        legend.title =  element_blank(),
        legend.direction = "horizontal",
        legend.position = c(0.5, 0.05)) +
  annotate("text", x = 2.5, y = 30, label = "CCCP", size = 8) +
  annotate("text", x = 6.5, y = 30, label = "Combo", size = 8) +
  annotate("text", x = 10.5, y = 30, label = "Ctrl", size = 8) +
  annotate("text", x = 14.5, y = 30, label = "USP30", size = 8) +
  ylim(-5, 30)

plots[[i]] <- p1
}
## [1] "O94826_K168"
## [1] "O94826_K300"
## [1] "P00387_K115"
## [1] "P05023_K0727"
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## [1] "P19367_K501"
## [1] "P61247_K056"
## [1] "Q14566_K611"
## [1] "Q15154_K0970"
## [1] "Q9BYC9_K114"
## [1] "Q9UHP3_K0201"
## [1] "Q9Y490_K0334"
plots
## $O94826_K168
## Warning: Removed 26 row(s) containing missing values (geom_path).
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## 
## $O94826_K300
## Warning: Removed 4 row(s) containing missing values (geom_path).
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## $P00387_K115
## Warning: Removed 42 row(s) containing missing values (geom_path).
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## 
## $P05023_K0727
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## $P14618_K207
## Warning: Removed 14 row(s) containing missing values (geom_path).
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## 
## $P19367_K501
## Warning: Removed 2 row(s) containing missing values (geom_path).
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## $P61247_K056
## Warning: Removed 4 row(s) containing missing values (geom_path).
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## 
## $Q14566_K611

## 
## $Q15154_K0970
## Warning: Removed 14 row(s) containing missing values (geom_path).
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## $Q9BYC9_K114
## Warning: Removed 8 rows containing missing values (geom_point).

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## $Q9UHP3_K0201
## Warning: Removed 24 row(s) containing missing values (geom_path).
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## 
## $Q9Y490_K0334
## Warning: Removed 2 row(s) containing missing values (geom_path).
## Warning: Removed 4 rows containing missing values (geom_point).

plots <- list()
for (i in ptm_list2){
prot_ = str_split(i, "_")[[1]][1]
site_ = str_split(i, "_")[[1]][2]

pepform_df <- longFormat(pe[,,"pepformRel"], rowvars = c("protein", "ptm"), colvars = c("group")) %>% as.data.frame()
pepform_df <- pepform_df %>% filter(protein == prot_)
pepform_df <- pepform_df %>% filter(grepl(site_, ptm, fixed = T))
pepform_df$FeatureType <- "Peptide"
pepform_df <- pepform_df %>% arrange(group, rowname)

ptm_df <- longFormat(pe[,,"ptmRel"], rowvars = c("protein", "ptm"), colvars = c("group")) %>% as.data.frame()
ptm_df <- ptm_df %>% filter(ptm == i)
ptm_df$FeatureType <- "PTM"
ptm_df <- ptm_df %>% arrange(group, rowname)

plot_df <- rbindlist(list(pepform_df, ptm_df), fill = TRUE)

plot_df[plot_df$FeatureType == 'PTM'][['FeatureType']] <- "PTM Summarized"
plot_df[plot_df$FeatureType == 'Peptide'][['FeatureType']] <- "PTM Feature"
plot_df$primary <- forcats::fct_inorder(plot_df$primary)

p1 <- plot_df %>% ggplot() +
  geom_line(aes(x = primary, y = value , group = rowname, color = FeatureType), size =2) +
  geom_point(aes(x = primary, y = value , group = rowname, color = FeatureType), size = 5) +
  geom_vline(data=data.frame(x = c(4.5, 8.5, 12.5)),
             aes(xintercept=as.numeric(x)), linetype = "dashed") +
  scale_colour_manual(values = c("#C3C3C3", "#D55E00")) +
  #scale_size_manual(values = c(1, 2)) +
  labs(title = i, x = "BioReplicate", y = "Abundance") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 60, hjust=1, size = 16),
        axis.text.y = element_text(size = 16),
        legend.text=element_text(size=10),
        axis.title.y = element_text(size = 22),
        axis.title.x = element_text(size = 22),
        title = element_text(size = 22),
        strip.text = element_text(size = 16),
        legend.title =  element_blank(),
        legend.direction = "horizontal",
        legend.position = c(0.5, 0)) +
  annotate("text", x = 2.5, y = 6.5, label = "Ctrl", size = 6) +
  annotate("text", x = 6.5, y = 6.5, label = "CCCP", size = 6) +
  annotate("text", x = 10.5, y = 6.5, label = "Combo", size = 6) +
  annotate("text", x = 14.5, y = 6.5, label = "USP30", size = 6) +
  ylim(-6, 6.5)

plots[[i]] <- p1
}
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
## Warning: 'experiments' dropped; see 'metadata'
## harmonizing input:
##   removing 128 sampleMap rows not in names(experiments)
plots
## $O43242_K273

## 
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2.0.2 For MSstats significant ptms

Plots of MSstats first 40 significant PTMs

## Plot fourty ptm's from Combo vs Ctrl
plots <- c()
for (i in ptm_list1){
  print(i)
  if(!i %in% rownames(assay(pe[["ptmRel"]]))){
    print(paste(i, "not in msqrob"))
    next
    }
  if(i %in% ptm_list2){print(paste(i, "also significant with msqrob"))}
  prot <- strsplit(i, "_")[[1]][1]

#Protein abundance
protein <- assay(pe[["proteinRaw"]])[prot,] %>% as.data.frame() 
colnames(protein) <- "LogIntensities"
protein <- protein %>%
  mutate(Protein = prot,
         FeatureType = "Protein",
         GROUP = sapply(strsplit(rownames(protein), "-"), function(x) x[1])) %>%
         rownames_to_column("originalRUN") %>%
         arrange(GROUP)

#Peptidoform abundance
pepforms <- rowData(pe[["pepformRel"]]) %>% as.data.frame() %>% 
    filter(ptm == i) %>% rownames
pepform <- assay(pe[["pepformRel"]])[pepforms,] %>% as.data.frame()
if((nrow(rowData(pe[["peptidoformRaw"]]) %>% as.data.frame() %>% 
         filter(ptm == i))==0)){next}
if (length(pepforms)==0){
  print(paste(i, "not in normalised PTM data"))
  pepforms <- rowData(pe[["peptidoformRaw"]]) %>% as.data.frame() %>% 
    filter(ptm == i) %>% rownames
  pepform <- assay(pe[["peptidoformRaw"]])[pepforms,] %>% as.data.frame()
  if (length(pepforms)>1){
  pepform <- pepform %>% rownames_to_column("Protein") %>% 
    pivot_longer(cols = colnames(pepform), names_to = "originalRUN",
                 values_to = "LogIntensities") 
  pepform$GROUP <- sapply(strsplit(pepform$originalRUN, "-"), function(x) x[1])
  pepform <- pepform %>% arrange(pepform, GROUP)
} else {colnames(pepform) <- "LogIntensities"
        pepform <- pepform %>% rownames_to_column("originalRUN") 
        pepform <- pepform %>% mutate(GROUP = 
                    sapply(strsplit(pepform$originalRUN, "-"), function(x) x[1]),
                    Protein = pepforms[1]) %>%
                   arrange(GROUP)
        }
  pepform <- pepform %>%
  mutate(FeatureType = "pepform - unnormalised")
  plot_df <- rbindlist(list(protein, pepform), fill = TRUE)
  p1 <- plot_df %>% ggplot() +
  geom_line(aes(x = originalRUN, y = LogIntensities , group = Protein, color = FeatureType), size =2) +
  geom_point(aes(x = originalRUN, y = LogIntensities , group = Protein, color = FeatureType), size = 5) +
  geom_vline(data=data.frame(x = c(4.5, 8.5, 12.5)),
             aes(xintercept=as.numeric(x)), linetype = "dashed") +
  scale_colour_manual(values = c("pepform - unnormalised" = "lightgoldenrod", "Protein" = "palegreen2", 
                                 "PTM" = "palevioletred2", "PTM_estimated" = "deeppink4")) +
  #scale_size_manual(values = c(1, 2)) +
  labs(title = paste(i, "no normalisation possible"), x = "BioReplicate", y = "LogIntensity") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 60, hjust=1, size = 16),
        axis.text.y = element_text(size = 16),
        legend.text=element_text(size=10),
        axis.title.y = element_text(size = 22),
        axis.title.x = element_text(size = 22),
        title = element_text(size = 22),
        strip.text = element_text(size = 16),
        legend.title =  element_blank(),
        legend.direction = "horizontal",
        legend.position = c(0.5, 0.05)) +
  annotate("text", x = 2.5, y = 27, label = "CCCP", size = 8) +
  annotate("text", x = 6.5, y = 27, label = "Combo", size = 8) +
  annotate("text", x = 10.5, y = 27, label = "Ctrl", size = 8) +
  annotate("text", x = 14.5, y = 27, label = "USP30", size = 8) +
  ylim(-7, 27)

plots[[i]] <- p1
next
}
if (length(pepforms)>1){
  pepform <- pepform %>% rownames_to_column("Protein") %>% 
    pivot_longer(cols = colnames(pepform), names_to = "originalRUN",
                 values_to = "LogIntensities") 
  pepform$GROUP <- sapply(strsplit(pepform$originalRUN, "-"), function(x) x[1])
  pepform <- pepform %>% arrange(pepform, GROUP) %>%
    mutate(Protein = paste(Protein, "normalised"))
} else {colnames(pepform) <- "LogIntensities"
        pepform <- pepform %>% rownames_to_column("originalRUN") 
        pepform <- pepform %>% mutate(GROUP = 
                    sapply(strsplit(pepform$originalRUN, "-"), function(x) x[1]),
                    Protein = paste(pepforms[1], "normalised")) %>%
                   arrange(GROUP)
        }
pepform <- pepform %>%
  mutate(FeatureType = "Peptidoform - Normalised")

pepform$originalRUN <- forcats::fct_inorder(pepform$originalRUN)

pepforms_raw <- rowData(pe[["peptidoformRaw"]]) %>% as.data.frame() %>% 
    filter(ptm == i) %>% rownames
pepform_raw <- assay(pe[["peptidoformRaw"]])[pepforms_raw,] %>% as.data.frame()
if (length(pepforms_raw)>1){
  pepform_raw <- pepform_raw %>% rownames_to_column("Protein") %>% 
    pivot_longer(cols = colnames(pepform_raw), names_to = "originalRUN",
                 values_to = "LogIntensities") 
  pepform_raw$GROUP <- sapply(strsplit(pepform_raw$originalRUN, "-"), function(x) x[1])
  pepform_raw <- pepform_raw %>% arrange(pepform_raw, GROUP)
} else {colnames(pepform_raw) <- "LogIntensities"
        pepform_raw <- pepform_raw %>% rownames_to_column("originalRUN") 
        pepform_raw <- pepform_raw %>% mutate(GROUP = 
                    sapply(strsplit(pepform_raw$originalRUN, "-"), function(x) x[1]),
                    Protein = pepforms_raw[1]) %>%
                   arrange(GROUP)
        }
pepform_raw <- pepform_raw %>%
  mutate(FeatureType = "Peptidoform")


#Normalised PTM abundance
ptm_df <- assay(pe[["ptmRel"]])[i,] %>% as.data.frame()
colnames(ptm_df) <- "LogIntensities"
ptm_df <- ptm_df %>%
  mutate(Protein = i,
         FeatureType = "PTM - normalised",
         GROUP = sapply(strsplit(rownames(ptm_df), "-"), function(x) x[1])) %>%
         rownames_to_column("originalRUN") %>%
         arrange(GROUP)


#PTM estimated
Protein <- paste(rep(i, nrow(ptm_df)), "estimate")
ptm_estimate <- tibble(Protein)
ptm_estimate$originalRUN <- ptm_df$originalRUN
ptm_estimate$GROUP <- ptm_df$GROUP
ptm_estimate$FeatureType <- "PTM_estimated"
ptm_estimate$LogIntensities <- NA

fixeffects <- rowData(pe[["ptmRel"]])$msqrobModels[[i]] %>% getCoef
if (! all(is.finite(fixeffects))){print(paste(i, "no refitting possible"))
  next}
ptm_estimate[ptm_estimate$GROUP=="Ctrl",]$LogIntensities <- 
                                                    fixeffects[["(Intercept)"]]
ptm_estimate[ptm_estimate$GROUP=="Combo",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("Combo", names(fixeffects))][1]
ptm_estimate[ptm_estimate$GROUP=="CCCP",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("CCCP", names(fixeffects))][1]
ptm_estimate[ptm_estimate$GROUP=="USP30_OE",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("USP30_OE", names(fixeffects))][1]


plot_df <- rbindlist(list(protein, pepform, ptm_df, ptm_estimate, pepform_raw), fill = TRUE)
plot_df$originalRUN <- as.vector(plot_df$originalRUN)
plot_df <- plot_df %>% arrange(FeatureType, originalRUN)
plot_df$originalRUN <- forcats::fct_inorder(plot_df$originalRUN)
plot_points <- plot_df %>% filter(FeatureType != "PTM_estimated")

#plot_df[plot_df$FeatureType == 'Model'][['FeatureType']] <- "PTM Summarized"
#plot_df[plot_df$FeatureType == 'Peptide'][['FeatureType']] <- "PTM Feature"

p1 <- plot_df %>% ggplot() +
  geom_line(aes(x = originalRUN, y = LogIntensities , group = Protein, color = FeatureType), size =2) +
  geom_point(data = plot_points, aes(x = originalRUN, y = LogIntensities , group = Protein, color = FeatureType), size = 5) +
  geom_vline(data=data.frame(x = c(4.5, 8.5, 12.5)),
             aes(xintercept=as.numeric(x)), linetype = "dashed") +
  scale_colour_manual(values = c("Peptidoform - normalised" = "#C3C3C3", 
                                 "Protein" = "palegreen2", "PTM - normalised" = "goldenrod2", 
                                 "PTM_estimated" = "deeppink4", "Peptidoform" = "gray36"))+
  #scale_size_manual(values = c(1, 2)) +
  labs(title = i, x = "BioReplicate", y = "LogIntensity") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 60, hjust=1, size = 16),
        axis.text.y = element_text(size = 16),
        legend.text=element_text(size=11.3),
        axis.title.y = element_text(size = 22),
        axis.title.x = element_text(size = 22),
        title = element_text(size = 22),
        strip.text = element_text(size = 16),
        legend.title =  element_blank(),
        legend.direction = "horizontal",
        legend.position = c(0.5, 0.05)) +
  annotate("text", x = 2.5, y = 30, label = "CCCP", size = 8) +
  annotate("text", x = 6.5, y = 30, label = "Combo", size = 8) +
  annotate("text", x = 10.5, y = 30, label = "Ctrl", size = 8) +
  annotate("text", x = 14.5, y = 30, label = "USP30", size = 8) +
  ylim(-5, 30)

plots[[i]] <- p1
}
## [1] "A0FGR9_K608"
## [1] "A0FGR9_K608 not in msqrob"
## [1] "B2RB47_K073"
## [1] "B2RB47_K073 not in msqrob"
## [1] "O00154_K205"
## [1] "O00159_K0578"
## [1] "O00170_K215"
## [1] "O00232_K262"
## [1] "O00267_K0627"
## [1] "O00410_K0806"
## [1] "O14972_K103"
## [1] "O14980_K0686"
## [1] "O15372_K274"
## [1] "O43175_K289"
## [1] "O43670_K100"
## [1] "O43847_K0993"
## [1] "O60260_K076"
## [1] "O60260_K220"
## [1] "O60260_K369"
## [1] "O60260_K408"
## [1] "O60260_K435"
## [1] "O60341_K492"
## [1] "O60361_K034"
## [1] "O60361_K041"
## [1] "O60361_K070"
## [1] "O60361_K109"
## [1] "O60361_K113"
## [1] "O60361_K113 no refitting possible"
## [1] "O60506_K336"
## [1] "O60749_K218"
## [1] "O60749_K516"
## [1] "O60832_K433"
## [1] "O60888_K082"
## [1] "O75131_K158"
## [1] "O75150_K440"
## [1] "O75400_K669"
## [1] "O75694_K0740"
## [1] "O75694_K0824"
## [1] "O75694_K0890"
## [1] "O75694_K0902"
## [1] "O75694_K0987"
## [1] "O75694_K1069"
## [1] "O75694_K1126"
plots
## $O00154_K205
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (geom_point).

## 
## $O00159_K0578
## Warning: Removed 9 row(s) containing missing values (geom_path).
## Warning: Removed 18 rows containing missing values (geom_point).

## 
## $O00170_K215
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 27 rows containing missing values (geom_point).

## 
## $O00232_K262
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Removed 27 rows containing missing values (geom_point).

## 
## $O00267_K0627
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (geom_point).

## 
## $O00410_K0806
## Warning: Removed 40 row(s) containing missing values (geom_path).
## Warning: Removed 92 rows containing missing values (geom_point).

## 
## $O14972_K103
## Warning: Removed 3 row(s) containing missing values (geom_path).
## Warning: Removed 3 rows containing missing values (geom_point).

## 
## $O14980_K0686
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (geom_point).

## 
## $O15372_K274
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 27 rows containing missing values (geom_point).

## 
## $O43175_K289
## Warning: Removed 6 rows containing missing values (geom_point).

## 
## $O43670_K100
## Warning: Removed 4 row(s) containing missing values (geom_path).
## Warning: Removed 16 rows containing missing values (geom_point).

## 
## $O43847_K0993
## Warning: Removed 8 row(s) containing missing values (geom_path).
## Warning: Removed 35 rows containing missing values (geom_point).

## 
## $O60260_K076
## Warning: Removed 4 row(s) containing missing values (geom_path).
## Warning: Removed 15 rows containing missing values (geom_point).

## 
## $O60260_K220
## Warning: Removed 12 row(s) containing missing values (geom_path).
## Warning: Removed 46 rows containing missing values (geom_point).

## 
## $O60260_K369

## 
## $O60260_K408
## Warning: Removed 4 row(s) containing missing values (geom_path).
## Warning: Removed 18 rows containing missing values (geom_point).

## 
## $O60260_K435
## Warning: Removed 16 row(s) containing missing values (geom_path).
## Warning: Removed 60 rows containing missing values (geom_point).

## 
## $O60341_K492
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 27 rows containing missing values (geom_point).

## 
## $O60361_K034
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Removed 27 rows containing missing values (geom_point).

## 
## $O60361_K041
## Warning: Removed 8 row(s) containing missing values (geom_path).
## Warning: Removed 34 rows containing missing values (geom_point).

## 
## $O60361_K070
## Warning: Removed 40 row(s) containing missing values (geom_path).
## Warning: Removed 71 rows containing missing values (geom_point).

## 
## $O60361_K109
## Warning: Removed 14 row(s) containing missing values (geom_path).
## Warning: Removed 30 rows containing missing values (geom_point).

## 
## $O60506_K336
## Warning: Removed 6 rows containing missing values (geom_point).

## 
## $O60749_K218
## Warning: Removed 3 rows containing missing values (geom_point).

## 
## $O60749_K516
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (geom_point).

## 
## $O60832_K433
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 25 rows containing missing values (geom_point).

## 
## $O60888_K082
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 27 rows containing missing values (geom_point).

## 
## $O75131_K158
## Warning: Removed 10 row(s) containing missing values (geom_path).
## Warning: Removed 40 rows containing missing values (geom_point).

## 
## $O75150_K440
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (geom_point).

## 
## $O75400_K669
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Removed 24 rows containing missing values (geom_point).

## 
## $O75694_K0740
## Warning: Removed 4 row(s) containing missing values (geom_path).
## Warning: Removed 22 rows containing missing values (geom_point).

## 
## $O75694_K0824
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (geom_point).

## 
## $O75694_K0890
## Warning: Removed 10 row(s) containing missing values (geom_path).
## Warning: Removed 45 rows containing missing values (geom_point).

## 
## $O75694_K0902
## Warning: Removed 3 rows containing missing values (geom_point).

## 
## $O75694_K0987
## Warning: Removed 4 row(s) containing missing values (geom_path).
## Warning: Removed 23 rows containing missing values (geom_point).

## 
## $O75694_K1069
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 27 rows containing missing values (geom_point).

## 
## $O75694_K1126
## Warning: Removed 4 row(s) containing missing values (geom_path).
## Warning: Removed 22 rows containing missing values (geom_point).

for (i in ptm_list1){
  print(i)
  if(!i %in% rownames(assay(pe[["ptmRel"]]))){
    print(paste(i, "not in msqrob"))
    next
    }
  print(rowData(pe[["ptmRel"]])$groupCombo[i,])
  }
## [1] "A0FGR9_K608"
## [1] "A0FGR9_K608 not in msqrob"
## [1] "B2RB47_K073"
## [1] "B2RB47_K073 not in msqrob"
## [1] "O00154_K205"
##             logFC se       df  t pval adjPval
## O00154_K205    NA NA 5.561565 NA   NA      NA
## [1] "O00159_K0578"
##              logFC se       df  t pval adjPval
## O00159_K0578    NA NA 6.566507 NA   NA      NA
## [1] "O00170_K215"
##             logFC se       df  t pval adjPval
## O00170_K215    NA NA 4.561565 NA   NA      NA
## [1] "O00232_K262"
##             logFC se       df  t pval adjPval
## O00232_K262    NA NA 4.561565 NA   NA      NA
## [1] "O00267_K0627"
##              logFC se       df  t pval adjPval
## O00267_K0627    NA NA 5.561565 NA   NA      NA
## [1] "O00410_K0806"
##                 logFC        se       df        t        pval   adjPval
## O00410_K0806 1.770579 0.5540462 11.23528 3.195725 0.008308075 0.3435982
## [1] "O14972_K103"
##                  logFC       se       df          t      pval adjPval
## O14972_K103 -0.3065211 1.961715 9.431339 -0.1562516 0.8791284       1
## [1] "O14980_K0686"
##              logFC se       df  t pval adjPval
## O14980_K0686    NA NA 5.561565 NA   NA      NA
## [1] "O15372_K274"
##             logFC se       df  t pval adjPval
## O15372_K274    NA NA 4.561565 NA   NA      NA
## [1] "O43175_K289"
##             logFC se       df  t pval adjPval
## O43175_K289    NA NA 9.837067 NA   NA      NA
## [1] "O43670_K100"
##                  logFC        se       df         t      pval adjPval
## O43670_K100 0.06658972 0.1662865 11.00365 0.4004519 0.6964889       1
## [1] "O43847_K0993"
##              logFC se       df  t pval adjPval
## O43847_K0993    NA NA 4.561565 NA   NA      NA
## [1] "O60260_K076"
##                 logFC       se       df         t      pval adjPval
## O60260_K076 0.9880622 1.108285 9.473871 0.8915238 0.3947268       1
## [1] "O60260_K220"
##                logFC       se       df        t      pval adjPval
## O60260_K220 1.105436 1.265646 10.37847 0.873417 0.4021929       1
## [1] "O60260_K369"
##                  logFC        se       df         t     pval adjPval
## O60260_K369 -0.5466616 0.3494623 11.87849 -1.564294 0.143986       1
## [1] "O60260_K408"
##                logFC        se       df        t       pval   adjPval
## O60260_K408 1.441877 0.6890223 10.47664 2.092643 0.06160434 0.9415948
## [1] "O60260_K435"
##             logFC se       df  t pval adjPval
## O60260_K435    NA NA 5.561565 NA   NA      NA
## [1] "O60341_K492"
##             logFC se       df  t pval adjPval
## O60341_K492    NA NA 4.561565 NA   NA      NA
## [1] "O60361_K034"
##             logFC se       df  t pval adjPval
## O60361_K034    NA NA 4.561565 NA   NA      NA
## [1] "O60361_K041"
##                  logFC        se       df         t      pval adjPval
## O60361_K041 -0.3289675 0.6228605 10.52565 -0.528156 0.6083458       1
## [1] "O60361_K070"
##             logFC se       df  t pval adjPval
## O60361_K070    NA NA 5.561565 NA   NA      NA
## [1] "O60361_K109"
##                logFC       se       df         t      pval adjPval
## O60361_K109 1.091522 1.167485 10.39397 0.9349342 0.3710344       1
## [1] "O60361_K113"
##             logFC se df  t pval adjPval
## O60361_K113    NA NA NA NA   NA      NA
## [1] "O60506_K336"
##                logFC       se       df        t      pval adjPval
## O60506_K336 5.267339 4.133408 8.609018 1.274333 0.2358665       1
## [1] "O60749_K218"
##                 logFC        se       df         t      pval adjPval
## O60749_K218 0.0931323 0.3960529 9.755213 0.2351512 0.8189539       1
## [1] "O60749_K516"
##             logFC se       df  t pval adjPval
## O60749_K516    NA NA 5.561565 NA   NA      NA
## [1] "O60832_K433"
##             logFC se       df  t pval adjPval
## O60832_K433    NA NA 5.561565 NA   NA      NA
## [1] "O60888_K082"
##             logFC se       df  t pval adjPval
## O60888_K082    NA NA 4.561565 NA   NA      NA
## [1] "O75131_K158"
##             logFC se       df  t pval adjPval
## O75131_K158    NA NA 5.561565 NA   NA      NA
## [1] "O75150_K440"
##             logFC se       df  t pval adjPval
## O75150_K440    NA NA 5.561565 NA   NA      NA
## [1] "O75400_K669"
##             logFC se       df  t pval adjPval
## O75400_K669    NA NA 5.561565 NA   NA      NA
## [1] "O75694_K0740"
##                   logFC       se       df          t      pval adjPval
## O75694_K0740 -0.7723166 1.695731 10.37557 -0.4554475 0.6581702       1
## [1] "O75694_K0824"
##              logFC se       df  t pval adjPval
## O75694_K0824    NA NA 5.561565 NA   NA      NA
## [1] "O75694_K0890"
##              logFC se       df  t pval adjPval
## O75694_K0890    NA NA 5.561565 NA   NA      NA
## [1] "O75694_K0902"
##                   logFC       se       df          t      pval adjPval
## O75694_K0902 -0.7680094 2.026458 9.437002 -0.3789911 0.7130815       1
## [1] "O75694_K0987"
##                    logFC        se       df          t      pval adjPval
## O75694_K0987 -0.07075939 0.4983178 9.585284 -0.1419965 0.8900228       1
## [1] "O75694_K1069"
##              logFC se       df  t pval adjPval
## O75694_K1069    NA NA 4.561565 NA   NA      NA
## [1] "O75694_K1126"
##                 logFC       se       df        t      pval adjPval
## O75694_K1126 2.708987 2.618607 10.43998 1.034515 0.3242647       1

2.1 Msqrob for MSstats summary

estimated ptm abundance -> rowData(peMSstatsSum[[“ptmNorm”]])\(msqrobModels\)P18124_K029 %>% getCoef() protein abundance -> summarized_ptm\(PROTEIN\)ProteinLevelData %>% filter(Protein == i) %>% pull(LogIntensities) ptm abundance -> assay(peMSstatsSum[[“ptm]]) of summarized_ptm\(PTM\)ProteinLevelData %>% filter(Protein == i) %>% pull(LogIntensities) normalised ptm abundance -> assay(peMSstatsSum[[“ptmNorm]])

## Plot significant ptm's from Combo vs Ctrl
ptm_list3 <- rowData(pe[["ptmMSstatsRel"]])$groupCombo %>% filter(adjPval <= 0.05) %>% rownames()
plots <- c()
for (i in ptm_list3){
  prot <- strsplit(i, "_")[[1]][1]

#Protein abundance
protein <- tibble(originalRUN, GROUP)
protein$FeatureType <- "Protein"
protein$Protein <- prot
tmp <- summarized_ptm$PROTEIN$ProteinLevelData %>% filter(Protein == prot) %>%
  select(c(LogIntensities, originalRUN))
missing_runs <- setdiff(originalRUN, tmp$originalRUN)
tmp2 <- tibble(originalRUN = missing_runs, LogIntensities = NA)
tmp3 <- rbind(tmp, tmp2) %>% arrange(originalRUN) %>% select(LogIntensities)
protein <- cbind(protein, tmp3)

#PTM abundance
ptm_df <- tibble(originalRUN, GROUP)
ptm_df$FeatureType <- "PTM"
ptm_df$Protein <- i
tmp <- summarized_ptm$PTM$ProteinLevelData %>% filter(Protein == i) %>% 
  select(c(LogIntensities, originalRUN))
missing_runs <- setdiff(originalRUN, tmp$originalRUN)
tmp2 <- tibble(originalRUN = missing_runs, LogIntensities = NA)
tmp3 <- rbind(tmp, tmp2) %>% arrange(originalRUN) %>% select(LogIntensities)
ptm_df <- cbind(ptm_df, tmp3)


#Normalised PTM abundance
ptm_df_norm <- assay(pe[["ptmMSstatsRel"]])[i,] %>% as.data.frame()
colnames(ptm_df_norm) <- "LogIntensities"
ptm_df_norm <- ptm_df_norm %>%
  mutate(Protein = i,
         FeatureType = "PTM - normalised",
         GROUP = sapply(strsplit(rownames(ptm_df_norm), "-"), function(x) x[1])) %>%
         rownames_to_column("originalRUN") %>%
         arrange(GROUP)


#PTM estimated
Protein <- rep(i, nrow(ptm_df))
ptm_estimate <- tibble(Protein)
ptm_estimate$originalRUN <- ptm_df$originalRUN
ptm_estimate$GROUP <- ptm_df$GROUP
ptm_estimate$FeatureType <- "PTM_estimated"
ptm_estimate$LogIntensities <- NA

fixeffects <- rowData(pe[["ptmMSstatsRel"]])$msqrobModels[[i]] %>% getCoef
ptm_estimate[ptm_estimate$GROUP=="Ctrl",]$LogIntensities <- 
                                                    fixeffects[["(Intercept)"]]
ptm_estimate[ptm_estimate$GROUP=="Combo",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("Combo", names(fixeffects))][1]
ptm_estimate[ptm_estimate$GROUP=="CCCP",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("CCCP", names(fixeffects))][1]
ptm_estimate[ptm_estimate$GROUP=="USP30_OE",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("USP30_OE", names(fixeffects))][1]


plot_df <- rbindlist(list(protein, ptm_df_norm, ptm_df, ptm_estimate), fill = TRUE)
plot_df <- plot_df %>% arrange(FeatureType)
plot_df$originalRUN <- forcats::fct_inorder(plot_df$originalRUN)
plot_points <- plot_df %>% filter(FeatureType != "PTM_estimated")

#plot_df[plot_df$FeatureType == 'Model'][['FeatureType']] <- "PTM Summarized"
#plot_df[plot_df$FeatureType == 'Peptide'][['FeatureType']] <- "PTM Feature"

p1 <- plot_df %>% ggplot() +
  geom_line(aes(x = originalRUN, y = LogIntensities , group = FeatureType, color = FeatureType), size =2) +
  geom_point(data = plot_points, aes(x = originalRUN, y = LogIntensities , group = FeatureType, color = FeatureType), size = 5) +
  geom_vline(data=data.frame(x = c(4.5, 8.5, 12.5)),
             aes(xintercept=as.numeric(x)), linetype = "dashed") +
    scale_colour_manual(values = c("PTM - normalised" = "lightgoldenrod", "Protein" = "skyblue3", 
                                 "PTM" = "seagreen3", "PTM_estimated" = "palevioletred2")) +
  #scale_size_manual(values = c(1, 2)) +
  labs(title = i, x = "BioReplicate", y = "LogIntensity") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 60, hjust=1, size = 16),
        axis.text.y = element_text(size = 16),
        legend.text=element_text(size=10),
        axis.title.y = element_text(size = 22),
        axis.title.x = element_text(size = 22),
        title = element_text(size = 22),
        strip.text = element_text(size = 16),
        legend.title =  element_blank(),
        legend.direction = "horizontal",
        legend.position = c(0.5, 0)) +
  annotate("text", x = 2.5, y = 27, label = "CCCP", size = 8) +
  annotate("text", x = 6.5, y = 27, label = "Combo", size = 8) +
  annotate("text", x = 10.5, y = 27, label = "Ctrl", size = 8) +
  annotate("text", x = 14.5, y = 27, label = "USP30", size = 8) +
  ylim(-7, 27)

plots[[i]] <- p1
}
plots
## $O60361_K109
## Warning: Removed 8 row(s) containing missing values (geom_path).
## Warning: Removed 8 rows containing missing values (geom_point).

## 
## $P23396_K062

## 
## $P23396_K075

## 
## $P32969_K121

## 
## $P46781_K066

## 
## $P53621_K0825
## Warning: Removed 2 rows containing missing values (geom_point).

## 
## $P62736_K328
## Warning: Removed 2 rows containing missing values (geom_point).

## 
## $Q07020_K049

## 
## $Q07955_K138

## 
## $Q14683_K0536

## 
## $Q9Y277_K053
## Warning: Removed 2 rows containing missing values (geom_point).

2.1.1 For MSstats significant ptms

## Plot fourty ptm's from Combo vs Ctrl
originalRUN <- c("CCCP-B1T1", "CCCP-B1T2", "CCCP-B2T1", "CCCP-B2T2", "Combo-B1T1", 
                 "Combo-B1T2", "Combo-B2T1", "Combo-B2T2", "Ctrl-B1T1", "Ctrl-B1T2",   
                 "Ctrl-B2T1", "Ctrl-B2T2", "USP30_OE-B1T1", "USP30_OE-B1T2", 
                 "USP30_OE-B2T1", "USP30_OE-B2T2")
GROUP <- sapply(strsplit(originalRUN, "-"), function(x) x[1])
plots <- c()
for (i in ptm_list1){
  if (i %in% ptm_list3){print(paste(i, "also significant in msqrob"))}
  prot <- strsplit(i, "_")[[1]][1]

#Protein abundance
protein <- tibble(originalRUN, GROUP)
protein$FeatureType <- "Protein"
protein$Protein <- prot
tmp <- summarized_ptm$PROTEIN$ProteinLevelData %>% filter(Protein == prot) %>%
  select(c(LogIntensities, originalRUN))
missing_runs <- setdiff(originalRUN, tmp$originalRUN)
tmp2 <- tibble(originalRUN = missing_runs, LogIntensities = NA)
tmp3 <- rbind(tmp, tmp2) %>% arrange(originalRUN) %>% select(LogIntensities)
protein <- cbind(protein, tmp3)


#PTM abundance
ptm_df <- tibble(originalRUN, GROUP)
ptm_df$FeatureType <- "PTM"
ptm_df$Protein <- i
tmp <- summarized_ptm$PTM$ProteinLevelData %>% filter(Protein == i) %>% 
  select(c(LogIntensities, originalRUN))
missing_runs <- setdiff(originalRUN, tmp$originalRUN)
tmp2 <- tibble(originalRUN = missing_runs, LogIntensities = NA)
tmp3 <- rbind(tmp, tmp2) %>% arrange(originalRUN) %>% select(LogIntensities)
ptm_df <- cbind(ptm_df, tmp3)

#Normalised PTM abundance
if (!i %in% rownames(assay(pe[["ptmMSstatsRel"]]))){
  print(paste(i, "not in normalised PTM data"))
  plot_df <- rbindlist(list(protein, ptm_df), fill = TRUE)
  p1 <- plot_df %>% ggplot() +
  geom_line(aes(x = originalRUN, y = LogIntensities , group = FeatureType, color = FeatureType), size =2) +
  geom_point(aes(x = originalRUN, y = LogIntensities , group = FeatureType, color = FeatureType), size = 5) +
  geom_vline(data=data.frame(x = c(4.5, 8.5, 12.5)),
             aes(xintercept=as.numeric(x)), linetype = "dashed") +
  scale_colour_manual(values = c("PTM - normalised" = "lightgoldenrod", "Protein" = "skyblue3", 
                                 "PTM" = "seagreen3", "PTM_estimated" = "palevioletred2")) +
  #scale_size_manual(values = c(1, 2)) +
  labs(title = paste(i, "no normalisation possible"), x = "BioReplicate", y = "LogIntensity") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 60, hjust=1, size = 16),
        axis.text.y = element_text(size = 16),
        legend.text=element_text(size=10),
        axis.title.y = element_text(size = 22),
        axis.title.x = element_text(size = 22),
        title = element_text(size = 22),
        strip.text = element_text(size = 16),
        legend.title =  element_blank(),
        legend.direction = "horizontal",
        legend.position = c(0.5, 0)) +
  annotate("text", x = 2.5, y = 27, label = "CCCP", size = 8) +
  annotate("text", x = 6.5, y = 27, label = "Combo", size = 8) +
  annotate("text", x = 10.5, y = 27, label = "Ctrl", size = 8) +
  annotate("text", x = 14.5, y = 27, label = "USP30", size = 8) +
  ylim(-7, 27)

plots[[i]] <- p1
next
}
ptm_df_norm <- assay(pe[["ptmMSstatsRel"]])[i,] %>% as.data.frame()
colnames(ptm_df_norm) <- "LogIntensities"
ptm_df_norm <- ptm_df_norm %>%
  mutate(Protein = i,
         FeatureType = "PTM - normalised",
         GROUP = sapply(strsplit(rownames(ptm_df_norm), "-"), function(x) x[1])) %>%
         rownames_to_column("originalRUN") %>%
         arrange(GROUP)


#PTM estimated
Protein <- rep(i, nrow(ptm_df))
ptm_estimate <- tibble(Protein)
ptm_estimate$originalRUN <- ptm_df$originalRUN
ptm_estimate$GROUP <- ptm_df$GROUP
ptm_estimate$FeatureType <- "PTM_estimated"
ptm_estimate$LogIntensities <- NA

if(rowData(pe[["ptmMSstatsRel"]])$msqrobModels[[i]] %>% getFitMethod() == "fitError"){
    print(paste(i, "resulted in a fitError"))
  plot_df <- rbindlist(list(protein, ptm_df, ptm_df_norm), fill = TRUE)
  p1 <- plot_df %>% ggplot() +
  geom_line(aes(x = originalRUN, y = LogIntensities , group = FeatureType, color = FeatureType), size =2) +
  geom_point(aes(x = originalRUN, y = LogIntensities , group = FeatureType, color = FeatureType), size = 5) +
  geom_vline(data=data.frame(x = c(4.5, 8.5, 12.5)),
             aes(xintercept=as.numeric(x)), linetype = "dashed") +
  scale_colour_manual(values = c("PTM - normalised" = "lightgoldenrod", "Protein" = "skyblue3", 
                                 "PTM" = "seagreen3", "PTM_estimated" = "palevioletred2")) +
  #scale_size_manual(values = c(1, 2)) +
  labs(title = paste(i, "fitError"), x = "BioReplicate", y = "LogIntensity") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 60, hjust=1, size = 16),
        axis.text.y = element_text(size = 16),
        legend.text=element_text(size=10),
        axis.title.y = element_text(size = 22),
        axis.title.x = element_text(size = 22),
        title = element_text(size = 22),
        strip.text = element_text(size = 16),
        legend.title =  element_blank(),
        legend.direction = "horizontal",
        legend.position = c(0.5, 0)) +
  annotate("text", x = 2.5, y = 27, label = "CCCP", size = 8) +
  annotate("text", x = 6.5, y = 27, label = "Combo", size = 8) +
  annotate("text", x = 10.5, y = 27, label = "Ctrl", size = 8) +
  annotate("text", x = 14.5, y = 27, label = "USP30", size = 8) +
  ylim(-7, 27)

plots[[i]] <- p1
  next
}
fixeffects <- rowData(pe[["ptmMSstatsRel"]])$msqrobModels[[i]] %>% getCoef
try(ptm_estimate[ptm_estimate$GROUP=="Ctrl",]$LogIntensities <- 
                                                    fixeffects[["(Intercept)"]])
try(ptm_estimate[ptm_estimate$GROUP=="Combo",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("Combo", names(fixeffects))][1])
try(ptm_estimate[ptm_estimate$GROUP=="CCCP",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("CCCP", names(fixeffects))][1])
try(ptm_estimate[ptm_estimate$GROUP=="USP30_OE",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("USP30_OE", names(fixeffects))][1])


plot_df <- rbindlist(list(protein, ptm_df_norm, ptm_df, ptm_estimate), fill = TRUE)
plot_df <- plot_df %>% arrange(FeatureType)
plot_df$originalRUN <- forcats::fct_inorder(plot_df$originalRUN)
plot_points <- plot_df %>% filter(FeatureType != "PTM_estimated")

#plot_df[plot_df$FeatureType == 'Model'][['FeatureType']] <- "PTM Summarized"
#plot_df[plot_df$FeatureType == 'Peptide'][['FeatureType']] <- "PTM Feature"

p1 <- plot_df %>% ggplot() +
  geom_line(aes(x = originalRUN, y = LogIntensities , group = FeatureType, color = FeatureType), size =2) +
  geom_point(data = plot_points, aes(x = originalRUN, y = LogIntensities , group = FeatureType, color = FeatureType), size = 5) +
  geom_vline(data=data.frame(x = c(4.5, 8.5, 12.5)),
             aes(xintercept=as.numeric(x)), linetype = "dashed") +
  scale_colour_manual(values = c("PTM - normalised" = "lightgoldenrod", "Protein" = "skyblue3", 
                                 "PTM" = "seagreen3", "PTM_estimated" = "palevioletred2")) +
  #scale_size_manual(values = c(1, 2)) +
  labs(title = i, x = "BioReplicate", y = "LogIntensity") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 60, hjust=1, size = 16),
        axis.text.y = element_text(size = 16),
        legend.text=element_text(size=10),
        axis.title.y = element_text(size = 22),
        axis.title.x = element_text(size = 22),
        title = element_text(size = 22),
        strip.text = element_text(size = 16),
        legend.title =  element_blank(),
        legend.direction = "horizontal",
        legend.position = c(0.5, 0)) +
  annotate("text", x = 2.5, y = 30, label = "CCCP", size = 8) +
  annotate("text", x = 6.5, y = 30, label = "Combo", size = 8) +
  annotate("text", x = 10.5, y = 30, label = "Ctrl", size = 8) +
  annotate("text", x = 14.5, y = 30, label = "USP30", size = 8) +
  ylim(-10, 30)

plots[[i]] <- p1
}
## [1] "A0FGR9_K608 resulted in a fitError"
## [1] "B2RB47_K073 resulted in a fitError"
## [1] "O14980_K0686 resulted in a fitError"
## Error in fixeffects[["(Intercept)"]] : subscript out of bounds
## Error in fixeffects[["(Intercept)"]] : subscript out of bounds
## Error in fixeffects[["(Intercept)"]] : subscript out of bounds
## Error in fixeffects[["(Intercept)"]] : subscript out of bounds
## [1] "O60341_K492 resulted in a fitError"
## [1] "O60361_K109 also significant in msqrob"
## Error in fixeffects[["(Intercept)"]] : subscript out of bounds
## Error in fixeffects[["(Intercept)"]] : subscript out of bounds
## Error in fixeffects[["(Intercept)"]] : subscript out of bounds
## Error in fixeffects[["(Intercept)"]] : subscript out of bounds
## [1] "O60749_K516 resulted in a fitError"
## [1] "O75131_K158 resulted in a fitError"
## [1] "O75150_K440 resulted in a fitError"
## [1] "O75694_K0824 resulted in a fitError"
## [1] "O75694_K1069 resulted in a fitError"
plots
## $A0FGR9_K608
## Warning: Removed 20 row(s) containing missing values (geom_path).
## Warning: Removed 32 rows containing missing values (geom_point).

## 
## $B2RB47_K073
## Warning: Removed 28 row(s) containing missing values (geom_path).
## Warning: Removed 36 rows containing missing values (geom_point).

## 
## $O00154_K205
## Warning: Removed 4 row(s) containing missing values (geom_path).
## Warning: Removed 17 rows containing missing values (geom_point).

## 
## $O00159_K0578
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 12 rows containing missing values (geom_point).

## 
## $O00170_K215
## Warning: Removed 4 row(s) containing missing values (geom_path).
## Warning: Removed 18 rows containing missing values (geom_point).

## 
## $O00232_K262
## Warning: Removed 4 row(s) containing missing values (geom_path).
## Removed 18 rows containing missing values (geom_point).

## 
## $O00267_K0627
## Warning: Removed 4 row(s) containing missing values (geom_path).
## Warning: Removed 16 rows containing missing values (geom_point).

## 
## $O00410_K0806

## 
## $O14972_K103
## Warning: Removed 5 row(s) containing missing values (geom_path).
## Warning: Removed 17 rows containing missing values (geom_point).

## 
## $O14980_K0686
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (geom_point).

## 
## $O15372_K274
## Warning: Removed 20 row(s) containing missing values (geom_path).
## Warning: Removed 20 rows containing missing values (geom_point).

## 
## $O43175_K289
## Warning: Removed 4 rows containing missing values (geom_point).

## 
## $O43670_K100
## Warning: Removed 4 row(s) containing missing values (geom_path).
## Warning: Removed 16 rows containing missing values (geom_point).

## 
## $O43847_K0993
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 26 rows containing missing values (geom_point).

## 
## $O60260_K076
## Warning: Removed 2 rows containing missing values (geom_point).

## 
## $O60260_K220

## 
## $O60260_K369

## 
## $O60260_K408

## 
## $O60260_K435
## Warning: Removed 4 row(s) containing missing values (geom_path).
## Warning: Removed 16 rows containing missing values (geom_point).

## 
## $O60341_K492
## Warning: Removed 20 row(s) containing missing values (geom_path).
## Warning: Removed 34 rows containing missing values (geom_point).

## 
## $O60361_K034
## Warning: Removed 4 row(s) containing missing values (geom_path).
## Warning: Removed 18 rows containing missing values (geom_point).

## 
## $O60361_K041

## 
## $O60361_K070
## Warning: Removed 4 row(s) containing missing values (geom_path).
## Warning: Removed 16 rows containing missing values (geom_point).

## 
## $O60361_K109

## 
## $O60361_K113
## Warning: Removed 30 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (geom_point).

## 
## $O60506_K336
## Warning: Removed 4 rows containing missing values (geom_point).

## 
## $O60749_K218
## Warning: Removed 4 row(s) containing missing values (geom_path).
## Warning: Removed 17 rows containing missing values (geom_point).

## 
## $O60749_K516
## Warning: Removed 20 row(s) containing missing values (geom_path).
## Warning: Removed 32 rows containing missing values (geom_point).

## 
## $O60832_K433
## Warning: Removed 4 row(s) containing missing values (geom_path).
## Warning: Removed 17 rows containing missing values (geom_point).

## 
## $O60888_K082
## Warning: Removed 4 row(s) containing missing values (geom_path).
## Warning: Removed 18 rows containing missing values (geom_point).

## 
## $O75131_K158
## Warning: Removed 24 row(s) containing missing values (geom_path).
## Warning: Removed 35 rows containing missing values (geom_point).

## 
## $O75150_K440
## Warning: Removed 24 row(s) containing missing values (geom_path).
## Removed 35 rows containing missing values (geom_point).

## 
## $O75400_K669
## Warning: Removed 4 row(s) containing missing values (geom_path).
## Warning: Removed 16 rows containing missing values (geom_point).

## 
## $O75694_K0740
## Warning: Removed 4 row(s) containing missing values (geom_path).
## Removed 16 rows containing missing values (geom_point).

## 
## $O75694_K0824
## Warning: Removed 20 row(s) containing missing values (geom_path).
## Warning: Removed 32 rows containing missing values (geom_point).

## 
## $O75694_K0890
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (geom_point).

## 
## $O75694_K0902
## Warning: Removed 4 row(s) containing missing values (geom_path).
## Warning: Removed 18 rows containing missing values (geom_point).

## 
## $O75694_K0987
## Warning: Removed 4 row(s) containing missing values (geom_path).
## Removed 18 rows containing missing values (geom_point).

## 
## $O75694_K1069
## Warning: Removed 20 row(s) containing missing values (geom_path).
## Warning: Removed 33 rows containing missing values (geom_point).

## 
## $O75694_K1126
## Warning: Removed 4 row(s) containing missing values (geom_path).
## Warning: Removed 16 rows containing missing values (geom_point).

for (i in ptm_list1){
  print(i)
  print(rowData(pe[["ptmMSstatsRel"]])$groupCombo[i,])
  }
## [1] "A0FGR9_K608"
##             logFC se df  t pval adjPval
## A0FGR9_K608    NA NA NA NA   NA      NA
## [1] "B2RB47_K073"
##             logFC se df  t pval adjPval
## B2RB47_K073    NA NA NA NA   NA      NA
## [1] "O00154_K205"
##             logFC se       df  t pval adjPval
## O00154_K205    NA NA 6.115833 NA   NA      NA
## [1] "O00159_K0578"
##              logFC se       df  t pval adjPval
## O00159_K0578    NA NA 6.993305 NA   NA      NA
## [1] "O00170_K215"
##             logFC se       df  t pval adjPval
## O00170_K215    NA NA 5.115833 NA   NA      NA
## [1] "O00232_K262"
##             logFC se       df  t pval adjPval
## O00232_K262    NA NA 5.115833 NA   NA      NA
## [1] "O00267_K0627"
##              logFC se       df  t pval adjPval
## O00267_K0627    NA NA 6.115833 NA   NA      NA
## [1] "O00410_K0806"
##                 logFC       se       df        t       pval   adjPval
## O00410_K0806 3.075823 1.418033 11.07609 2.169077 0.05269811 0.7011021
## [1] "O14972_K103"
##             logFC se       df  t pval adjPval
## O14972_K103    NA NA 6.115833 NA   NA      NA
## [1] "O14980_K0686"
##              logFC se df  t pval adjPval
## O14980_K0686    NA NA NA NA   NA      NA
## [1] "O15372_K274"
##             logFC se df  t pval adjPval
## O15372_K274    NA NA NA NA   NA      NA
## [1] "O43175_K289"
##             logFC se      df  t pval adjPval
## O43175_K289    NA NA 9.88883 NA   NA      NA
## [1] "O43670_K100"
##             logFC se       df  t pval adjPval
## O43670_K100    NA NA 6.115833 NA   NA      NA
## [1] "O43847_K0993"
##              logFC se       df  t pval adjPval
## O43847_K0993    NA NA 5.115833 NA   NA      NA
## [1] "O60260_K076"
##                logFC       se       df        t      pval   adjPval
## O60260_K076 2.947903 1.078911 10.05118 2.732297 0.0210186 0.5439243
## [1] "O60260_K220"
##                logFC        se       df        t        pval   adjPval
## O60260_K220 2.945495 0.8255802 11.13382 3.567788 0.004331355 0.2309399
## [1] "O60260_K369"
##                logFC        se       df       t        pval  adjPval
## O60260_K369 1.211412 0.2863608 12.50321 4.23037 0.001068061 0.104403
## [1] "O60260_K408"
##                logFC        se       df        t        pval   adjPval
## O60260_K408 3.212302 0.8237631 11.05165 3.899546 0.002457055 0.1742382
## [1] "O60260_K435"
##             logFC se       df  t pval adjPval
## O60260_K435    NA NA 6.115833 NA   NA      NA
## [1] "O60341_K492"
##             logFC se df  t pval adjPval
## O60341_K492    NA NA NA NA   NA      NA
## [1] "O60361_K034"
##             logFC se       df  t pval adjPval
## O60361_K034    NA NA 5.115833 NA   NA      NA
## [1] "O60361_K041"
##                logFC       se       df        t      pval   adjPval
## O60361_K041 1.485787 1.261051 11.04014 1.178213 0.2634839 0.9996807
## [1] "O60361_K070"
##             logFC se       df  t pval adjPval
## O60361_K070    NA NA 6.115833 NA   NA      NA
## [1] "O60361_K109"
##                logFC        se       df        t         pval    adjPval
## O60361_K109 2.924282 0.5490288 11.26627 5.326281 0.0002235512 0.02622256
## [1] "O60361_K113"
##             logFC se df  t pval adjPval
## O60361_K113    NA NA NA NA   NA      NA
## [1] "O60506_K336"
##                logFC       se       df        t      pval   adjPval
## O60506_K336 5.149196 3.801191 9.167772 1.354627 0.2079758 0.9996807
## [1] "O60749_K218"
##             logFC se       df  t pval adjPval
## O60749_K218    NA NA 6.115833 NA   NA      NA
## [1] "O60749_K516"
##             logFC se df  t pval adjPval
## O60749_K516    NA NA NA NA   NA      NA
## [1] "O60832_K433"
##             logFC se       df  t pval adjPval
## O60832_K433    NA NA 6.115833 NA   NA      NA
## [1] "O60888_K082"
##             logFC se       df  t pval adjPval
## O60888_K082    NA NA 5.115833 NA   NA      NA
## [1] "O75131_K158"
##             logFC se df  t pval adjPval
## O75131_K158    NA NA NA NA   NA      NA
## [1] "O75150_K440"
##             logFC se df  t pval adjPval
## O75150_K440    NA NA NA NA   NA      NA
## [1] "O75400_K669"
##             logFC se       df  t pval adjPval
## O75400_K669    NA NA 6.115833 NA   NA      NA
## [1] "O75694_K0740"
##              logFC se       df  t pval adjPval
## O75694_K0740    NA NA 6.115833 NA   NA      NA
## [1] "O75694_K0824"
##              logFC se df  t pval adjPval
## O75694_K0824    NA NA NA NA   NA      NA
## [1] "O75694_K0890"
##              logFC se       df  t pval adjPval
## O75694_K0890    NA NA 6.115833 NA   NA      NA
## [1] "O75694_K0902"
##              logFC se       df  t pval adjPval
## O75694_K0902    NA NA 5.115833 NA   NA      NA
## [1] "O75694_K0987"
##              logFC se       df  t pval adjPval
## O75694_K0987    NA NA 5.115833 NA   NA      NA
## [1] "O75694_K1069"
##              logFC se df  t pval adjPval
## O75694_K1069    NA NA NA NA   NA      NA
## [1] "O75694_K1126"
##              logFC se       df  t pval adjPval
## O75694_K1126    NA NA 6.115833 NA   NA      NA
---
title: "Ubiquitination visualisation"
output:
  html_document:
    code_download: yes
    theme: cosmo
    toc: yes
    toc_depth: 4
    toc_float:
      collapsed: yes
    highlight: tango
    number_sections: yes
    df_print: paged
  pdf_document:
    toc: yes
    toc_depth: '4'
---

```{r}
library(tidyverse)
library(MSstatsPTM)
library(lme4)
library(msqrob2)
library(data.table)
```


Load in all data

```{r}
directory = "C:/Users/Nina/OneDrive - UGent/Documenten/Doctoraat/msqrobPTM paper/biological ubiquitin dataset/"
#MSStats data
load(paste0(directory, "MSstatsSummarizedData.RData"))
load(paste0(directory, "MSstatsModel.rda"))
#Msqrob
load("pe_ubiquitin_msqrob.rda")
```


# MSstats

estimated ptm abundance ->fixef(model_ptm$Model.Details$PTM[[100]]) 
protein abundance (for protein i) -> summarized_ptm$PROTEIN$ProteinLevelData %>% filter(Protein == i) %>% pull(LogIntensities)
ptm abundance (for ptm i) -> summarized_ptm$PTM$ProteinLevelData %>% filter(Protein == i) %>% pull(LogIntensities)
normalised ptm abundance ->  model_ptm$Model.Details$PTM[[100]] - model_ptm$Model.Details$PROTEIN[[100]]

At the moment, only in the GitHub version, a detailed model gets outputted with info on the
estimates on everything, so I used that here


```{r, fig.width=8, fig.height=10}
originalRUN <- c("CCCP-B1T1", "CCCP-B1T2", "CCCP-B2T1", "CCCP-B2T2", "Combo-B1T1", 
                 "Combo-B1T2", "Combo-B2T1", "Combo-B2T2", "Ctrl-B1T1", "Ctrl-B1T2",   
                 "Ctrl-B2T1", "Ctrl-B2T2", "USP30_OE-B1T1", "USP30_OE-B1T2", 
                 "USP30_OE-B2T1", "USP30_OE-B2T2")
GROUP <- sapply(strsplit(originalRUN, "-"), function(x) x[1])
## Plot fourty ptm's from Combo vs Ctrl
ptm_list1 <- model_ptm$ADJUSTED.Model %>% filter(Label == "Combo vs Ctrl") %>% filter(adj.pvalue <= 0.05) %>% head(40) %>% pull(Protein)
all_ptms <- model_ptm$PTM.Model %>% pull(Protein) %>% unique()
all_proteins <- model_ptm$PROTEIN.Model %>% pull(Protein) %>% unique()
adjusted_ptms <- model_ptm$ADJUSTED.Model %>% pull(Protein) %>% unique()
plots <- c()
for (i in ptm_list1){
  print(i)
  prot <- strsplit(i, "_")[[1]][1]
  index <- which(all_ptms == i)
  index_prot <- which(all_proteins == prot)
  index_adjusted <- which(adjusted_ptms == i)
#Protein abundance
protein <- tibble(originalRUN, GROUP)
protein$FeatureType <- "Protein"
protein$Protein <- prot
tmp <- summarized_ptm$PROTEIN$ProteinLevelData %>% filter(Protein == prot) %>%
  select(c(LogIntensities, originalRUN))
missing_runs <- setdiff(originalRUN, tmp$originalRUN)
tmp2 <- tibble(originalRUN = missing_runs, LogIntensities = NA)
tmp3 <- rbind(tmp, tmp2) %>% arrange(originalRUN) %>% select(LogIntensities)
protein <- cbind(protein, tmp3)

#PTM abundance
ptm_df <- summarized_ptm$PTM$ProteinLevelData %>% filter(Protein == i)
ptm_df$Abundance <- ptm_df$LogIntensities
ptm_df$FeatureType <- "PTM"
ptm_df <- ptm_df %>% select(c("Protein", "LogIntensities", "originalRUN", 
                                "GROUP", "FeatureType"))

#PTM estimated (maar nog niet adjusted)
Protein <- rep(i, length(originalRUN))
ptm_estimate <- tibble(Protein)
ptm_estimate$originalRUN <- originalRUN
ptm_estimate$GROUP <- GROUP
ptm_estimate$FeatureType <- "PTM_estimated"
ptm_estimate$LogIntensities <- NA

#Check that model estimates are definitely from that PTM
if (class(model_ptm$Model.Details$PTM[[index]]) == "lm"){print(i)
  }else{
print(all(
  summarized_ptm$PTM$ProteinLevelData %>% filter(Protein == i) %>% pull(LogIntensities) ==
    getME(model_ptm$Model.Details$PTM[[index]], "y")
))}

if (class(model_ptm$Model.Details$PTM[[index]]) == "lm"){
fixeffects <- model_ptm$Model.Details$PTM[[index]]$coefficients
} else {fixeffects <- fixef(model_ptm$Model.Details$PTM[[index]])} 
ptm_estimate[ptm_estimate$GROUP=="CCCP",]$LogIntensities <- 
                                                    fixeffects[["(Intercept)"]]
try(ptm_estimate[ptm_estimate$GROUP=="Combo",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("Combo", names(fixeffects))])
try(ptm_estimate[ptm_estimate$GROUP=="Ctrl",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("Ctrl", names(fixeffects))])
try(ptm_estimate[ptm_estimate$GROUP=="USP30_OE",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("USP30_OE", names(fixeffects))])

#Protein estimated (maar nog niet adjusted)
Protein <- rep(prot, length(originalRUN))
prot_estimate <- tibble(Protein)
prot_estimate$originalRUN <- originalRUN
prot_estimate$GROUP <- GROUP
prot_estimate$FeatureType <- "Protein_estimated"
prot_estimate$LogIntensities <- NA

#Check that model estimates are definitely from that PTM
if (class(model_ptm$Model.Details$PROTEIN[[index_prot]]) == "lmerMod"){
  print(all(
  summarized_ptm$PROTEIN$ProteinLevelData %>% filter(Protein == prot) %>% pull(LogIntensities) ==
    getME(model_ptm$Model.Details$PROTEIN[[index_prot]], "y")))
}

if (class(model_ptm$Model.Details$PROTEIN[[index_prot]]) == "lm"){
fixeffects <- model_ptm$Model.Details$PROTEIN[[index_prot]]$coefficients
} else {fixeffects <- fixef(model_ptm$Model.Details$PROTEIN[[index_prot]])} 
prot_estimate[prot_estimate$GROUP=="CCCP",]$LogIntensities <- 
                                                    fixeffects[["(Intercept)"]]
try(prot_estimate[prot_estimate$GROUP=="Combo",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("Combo", names(fixeffects))])
try(prot_estimate[prot_estimate$GROUP=="Ctrl",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("Ctrl", names(fixeffects))])
try(prot_estimate[prot_estimate$GROUP=="USP30_OE",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("USP30_OE", names(fixeffects))])

#PTM normalised
Protein <- rep(i, length(originalRUN))
ptm_norm <- tibble(Protein)
ptm_norm$originalRUN <- originalRUN
ptm_norm$GROUP <- GROUP
ptm_norm$FeatureType <- "PTM_normalised"
ptm_norm$LogIntensities <- ptm_estimate$LogIntensities - prot_estimate$LogIntensities


plot_df <- rbindlist(list(protein, ptm_df, ptm_estimate, prot_estimate, ptm_norm), fill = TRUE)
plot_points <- rbindlist(list(protein, ptm_df), fill = TRUE)

#plot_df[plot_df$FeatureType == 'Model'][['FeatureType']] <- "PTM Summarized"
#plot_df[plot_df$FeatureType == 'Peptide'][['FeatureType']] <- "PTM Feature"

p1 <- plot_df %>% ggplot() +
  geom_line(aes(x = originalRUN, y = LogIntensities , group = FeatureType, color = FeatureType), size =2) +
  geom_point(data = plot_points, aes(x = originalRUN, y = LogIntensities , 
                                     group = FeatureType, color = FeatureType), size = 5) +
  geom_vline(data=data.frame(x = c(4.5, 8.5, 12.5)),
             aes(xintercept=as.numeric(x)), linetype = "dashed") +
    scale_colour_manual(values = c("PTM" = "palevioletred1", "PTM_estimated" = "deeppink4", 
                                   "Protein" = "palegreen2", 
                                 "Protein_estimated" = "seagreen4",
                                 "PTM_normalised" = "goldenrod2")) +
  #scale_size_manual(values = c(1, 2)) +
  labs(title = i, x = "BioReplicate", y = "LogIntensity") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 60, hjust=1, size = 16),
        axis.text.y = element_text(size = 16),
        legend.text=element_text(size=11.5),
        axis.title.y = element_text(size = 22),
        axis.title.x = element_text(size = 22),
        title = element_text(size = 22),
        strip.text = element_text(size = 16),
        legend.title =  element_blank(),
        legend.direction = "horizontal",
        legend.position = c(0.5, 0.05)) +
  annotate("text", x = 2.5, y = 30, label = "CCCP", size = 8) +
  annotate("text", x = 6.5, y = 30, label = "Combo", size = 8) +
  annotate("text", x = 10.5, y = 30, label = "Ctrl", size = 8) +
  annotate("text", x = 14.5, y = 30, label = "USP30", size = 8) +
  ylim(-6, 30)

plots[[i]] <- p1
}
plots
```

```{r}
model_ptm$ADJUSTED.Model %>% filter(Protein == "O60260_K369", Label == "Combo vs Ctrl")
```


# Msqrob

estimated ptm abundance -> rowData(pe[["ptmRel"]])$msqrobModels$P18124_K029 %>% getCoef()
protein abundance -> assay(pe[["proteinRobust"]])
ptm abundance -> this I do not have, is already normalised
normalised ptm abundance -> assay(pe[["ptmRel"]])


```{r, fig.width=8, fig.height=10}
## Plot ptm's from Combo vs Ctrl
ptm_list2 <- rowData(pe[["ptmRel"]])$groupCombo %>% filter(adjPval <= 0.05) %>% rownames()
plots <- c()
for (i in ptm_list2){
  print(i)
  prot <- strsplit(i, "_")[[1]][1]

#Protein abundance
protein <- assay(pe[["proteinRaw"]])[prot,] %>% as.data.frame() 
colnames(protein) <- "LogIntensities"
protein <- protein %>%
  mutate(Protein = prot,
         FeatureType = "Protein",
         GROUP = sapply(strsplit(rownames(protein), "-"), function(x) x[1])) %>%
         rownames_to_column("originalRUN") %>%
         arrange(GROUP, originalRUN)

#Peptidoform abundance
pepforms <- rowData(pe[["peptidoformRaw"]]) %>% as.data.frame() %>% 
    filter(ptm == i) %>% rownames
pepform <- assay(pe[["peptidoformRaw"]])[pepforms,] %>% as.data.frame()
if (length(pepforms)==0) {next}
if (length(pepforms)>1){
  pepform <- pepform %>% rownames_to_column("Protein") %>% 
    pivot_longer(cols = colnames(pepform), names_to = "originalRUN",
                 values_to = "LogIntensities") 
  pepform$GROUP <- sapply(strsplit(pepform$originalRUN, "-"), function(x) x[1])
  pepform <- pepform %>% arrange(GROUP, originalRUN)
} else {colnames(pepform) <- "LogIntensities"
        pepform <- pepform %>% rownames_to_column("originalRUN") 
        pepform <- pepform %>% mutate(GROUP = 
                    sapply(strsplit(pepform$originalRUN, "-"), function(x) x[1]),
                    Protein = pepforms[1]) %>%
                   arrange(GROUP, originalRUN)
        }
pepform <- pepform %>%
  mutate(FeatureType = "Peptidoform")
#pepform <- select(-c("pepform"))
pepform$originalRUN <- forcats::fct_inorder(pepform$originalRUN)

#Peptidoform abundance - normalised
pepforms <- rowData(pe[["pepformRel"]]) %>% as.data.frame() %>% 
    filter(ptm == i) %>% rownames
pepform_norm <- assay(pe[["pepformRel"]])[pepforms,] %>% as.data.frame()
if (length(pepforms)>1){
  pepform_norm <- pepform_norm %>% rownames_to_column("Protein") %>% 
    pivot_longer(cols = colnames(pepform_norm), names_to = "originalRUN",
                 values_to = "LogIntensities") 
  pepform_norm$GROUP <- sapply(strsplit(pepform_norm$originalRUN, "-"), function(x) x[1])
  pepform_norm <- pepform_norm %>% arrange(GROUP, originalRUN) %>%
    mutate(Protein = paste(Protein,"norm"))
} else {colnames(pepform_norm) <- "LogIntensities"
        pepform_norm <- pepform_norm %>% rownames_to_column("originalRUN") 
        pepform_norm <- pepform_norm %>% mutate(GROUP = 
                    sapply(strsplit(pepform_norm$originalRUN, "-"), function(x) x[1]),
                    Protein = paste(pepforms[1],"norm")) %>%
                   arrange(GROUP, originalRUN)
        }
pepform_norm <- pepform_norm %>%
  mutate(FeatureType = "Peptidoform - normalised")
#pepform <- select(-c("pepform"))
pepform_norm$originalRUN <- forcats::fct_inorder(pepform_norm$originalRUN)

#Normalised PTM abundance
ptm_df <- assay(pe[["ptmRel"]])[i,] %>% as.data.frame()
colnames(ptm_df) <- "LogIntensities"
ptm_df <- ptm_df %>%
  mutate(Protein = i,
         FeatureType = "PTM - normalised",
         GROUP = sapply(strsplit(rownames(ptm_df), "-"), function(x) x[1])) %>%
         rownames_to_column("originalRUN") %>%
         arrange(GROUP, originalRUN)


#PTM estimated
Protein <- paste(rep(i, nrow(ptm_df)), "estimate")
ptm_estimate <- tibble(Protein)
ptm_estimate$originalRUN <- ptm_df$originalRUN
ptm_estimate$GROUP <- ptm_df$GROUP
ptm_estimate$FeatureType <- "PTM_estimated"
ptm_estimate$LogIntensities <- NA

fixeffects <- rowData(pe[["ptmRel"]])$msqrobModels[[i]] %>% getCoef
ptm_estimate[ptm_estimate$GROUP=="Ctrl",]$LogIntensities <- 
                                                    fixeffects[["(Intercept)"]]
ptm_estimate[ptm_estimate$GROUP=="Combo",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("Combo", names(fixeffects))][1]
ptm_estimate[ptm_estimate$GROUP=="CCCP",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("CCCP", names(fixeffects))][1]
ptm_estimate[ptm_estimate$GROUP=="USP30_OE",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("USP30_OE", names(fixeffects))][1]
ptm_estimate <- ptm_estimate %>% arrange(GROUP, originalRUN)

plot_df <- rbindlist(list(protein, pepform, pepform_norm, ptm_df, ptm_estimate), fill = TRUE)
plot_df$originalRUN <- forcats::fct_inorder(plot_df$originalRUN)
plot_points <- plot_df %>% filter(FeatureType != "PTM_estimated")

#plot_df[plot_df$FeatureType == 'Model'][['FeatureType']] <- "PTM Summarized"
#plot_df[plot_df$FeatureType == 'Peptide'][['FeatureType']] <- "PTM Feature"

p1 <- plot_df %>% ggplot() +
  geom_line(aes(x = originalRUN, y = LogIntensities , group = Protein, color = FeatureType), size =2) +
  geom_point(data = plot_points, aes(x = originalRUN, y = LogIntensities , group = Protein, color = FeatureType), size = 5) +
  geom_vline(data=data.frame(x = c(4.5, 8.5, 12.5)),
             aes(xintercept=as.numeric(x)), linetype = "dashed") +
  scale_colour_manual(values = c("Peptidoform - normalised" = "#C3C3C3", 
                                 "Protein" = "dodgerblue2", "PTM - normalised" = "seagreen3", 
                                 "PTM_estimated" = "palevioletred2", "Peptidoform" = "gray36"))  +
  #scale_size_manual(values = c(1, 2)) +
  labs(title = i, x = "BioReplicate", y = "LogIntensity") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 60, hjust=1, size = 16),
        axis.text.y = element_text(size = 16),
        legend.text=element_text(size=10),
        axis.title.y = element_text(size = 22),
        axis.title.x = element_text(size = 22),
        title = element_text(size = 22),
        strip.text = element_text(size = 16),
        legend.title =  element_blank(),
        legend.direction = "horizontal",
        legend.position = c(0.5, 0.05)) +
  annotate("text", x = 2.5, y = 30, label = "CCCP", size = 8) +
  annotate("text", x = 6.5, y = 30, label = "Combo", size = 8) +
  annotate("text", x = 10.5, y = 30, label = "Ctrl", size = 8) +
  annotate("text", x = 14.5, y = 30, label = "USP30", size = 8) +
  ylim(-5, 30)

plots[[i]] <- p1
}
plots
```

### CCCP vs Ctrl

```{r, fig.width=8, fig.height=10}
## Plot ptm's from CCCP vs Ctrl
ptm_listCCCP <- rowData(pe[["ptmRel"]])$groupCCCP %>% filter(adjPval <= 0.05) %>% rownames()
plots <- c()
for (i in ptm_listCCCP){
  print(i)
  prot <- strsplit(i, "_")[[1]][1]

#Protein abundance
protein <- assay(pe[["proteinRaw"]])[prot,] %>% as.data.frame() 
colnames(protein) <- "LogIntensities"
protein <- protein %>%
  mutate(Protein = prot,
         FeatureType = "Protein",
         GROUP = sapply(strsplit(rownames(protein), "-"), function(x) x[1])) %>%
         rownames_to_column("originalRUN") %>%
         arrange(GROUP, originalRUN)

#Peptidoform abundance
pepforms <- rowData(pe[["peptidoformRaw"]]) %>% as.data.frame() %>% 
    filter(ptm == i) %>% rownames
pepform <- assay(pe[["peptidoformRaw"]])[pepforms,] %>% as.data.frame()
if (length(pepforms)==0) {next}
if (length(pepforms)>1){
  pepform <- pepform %>% rownames_to_column("Protein") %>% 
    pivot_longer(cols = colnames(pepform), names_to = "originalRUN",
                 values_to = "LogIntensities") 
  pepform$GROUP <- sapply(strsplit(pepform$originalRUN, "-"), function(x) x[1])
  pepform <- pepform %>% arrange(GROUP, originalRUN)
} else {colnames(pepform) <- "LogIntensities"
        pepform <- pepform %>% rownames_to_column("originalRUN") 
        pepform <- pepform %>% mutate(GROUP = 
                    sapply(strsplit(pepform$originalRUN, "-"), function(x) x[1]),
                    Protein = pepforms[1]) %>%
                   arrange(GROUP, originalRUN)
        }
pepform <- pepform %>%
  mutate(FeatureType = "Peptidoform")
#pepform <- select(-c("pepform"))
pepform$originalRUN <- forcats::fct_inorder(pepform$originalRUN)

#Peptidoform abundance - normalised
pepforms <- rowData(pe[["pepformRel"]]) %>% as.data.frame() %>% 
    filter(ptm == i) %>% rownames
pepform_norm <- assay(pe[["pepformRel"]])[pepforms,] %>% as.data.frame()
if (length(pepforms)>1){
  pepform_norm <- pepform_norm %>% rownames_to_column("Protein") %>% 
    pivot_longer(cols = colnames(pepform_norm), names_to = "originalRUN",
                 values_to = "LogIntensities") 
  pepform_norm$GROUP <- sapply(strsplit(pepform_norm$originalRUN, "-"), function(x) x[1])
  pepform_norm <- pepform_norm %>% arrange(GROUP, originalRUN) %>%
    mutate(Protein = paste(Protein,"norm"))
} else {colnames(pepform_norm) <- "LogIntensities"
        pepform_norm <- pepform_norm %>% rownames_to_column("originalRUN") 
        pepform_norm <- pepform_norm %>% mutate(GROUP = 
                    sapply(strsplit(pepform_norm$originalRUN, "-"), function(x) x[1]),
                    Protein = paste(pepforms[1],"norm")) %>%
                   arrange(GROUP, originalRUN)
        }
pepform_norm <- pepform_norm %>%
  mutate(FeatureType = "Peptidoform - normalised")
#pepform <- select(-c("pepform"))
pepform_norm$originalRUN <- forcats::fct_inorder(pepform_norm$originalRUN)

#Normalised PTM abundance
ptm_df <- assay(pe[["ptmRel"]])[i,] %>% as.data.frame()
colnames(ptm_df) <- "LogIntensities"
ptm_df <- ptm_df %>%
  mutate(Protein = i,
         FeatureType = "PTM - normalised",
         GROUP = sapply(strsplit(rownames(ptm_df), "-"), function(x) x[1])) %>%
         rownames_to_column("originalRUN") %>%
         arrange(GROUP, originalRUN)


#PTM estimated
Protein <- paste(rep(i, nrow(ptm_df)), "estimate")
ptm_estimate <- tibble(Protein)
ptm_estimate$originalRUN <- ptm_df$originalRUN
ptm_estimate$GROUP <- ptm_df$GROUP
ptm_estimate$FeatureType <- "PTM_estimated"
ptm_estimate$LogIntensities <- NA

fixeffects <- rowData(pe[["ptmRel"]])$msqrobModels[[i]] %>% getCoef
ptm_estimate[ptm_estimate$GROUP=="Ctrl",]$LogIntensities <- 
                                                    fixeffects[["(Intercept)"]]
ptm_estimate[ptm_estimate$GROUP=="Combo",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("Combo", names(fixeffects))][1]
ptm_estimate[ptm_estimate$GROUP=="CCCP",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("CCCP", names(fixeffects))][1]
ptm_estimate[ptm_estimate$GROUP=="USP30_OE",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("USP30_OE", names(fixeffects))][1]
ptm_estimate <- ptm_estimate %>% arrange(GROUP, originalRUN)

plot_df <- rbindlist(list(protein, pepform, pepform_norm, ptm_df, ptm_estimate), fill = TRUE)
plot_df$originalRUN <- forcats::fct_inorder(plot_df$originalRUN)
plot_points <- plot_df %>% filter(FeatureType != "PTM_estimated")

#plot_df[plot_df$FeatureType == 'Model'][['FeatureType']] <- "PTM Summarized"
#plot_df[plot_df$FeatureType == 'Peptide'][['FeatureType']] <- "PTM Feature"

p1 <- plot_df %>% ggplot() +
  geom_line(aes(x = originalRUN, y = LogIntensities , group = Protein, color = FeatureType), size =2) +
  geom_point(data = plot_points, aes(x = originalRUN, y = LogIntensities , group = Protein, color = FeatureType), size = 5) +
  geom_vline(data=data.frame(x = c(4.5, 8.5, 12.5)),
             aes(xintercept=as.numeric(x)), linetype = "dashed") +
  scale_colour_manual(values = c("Peptidoform - normalised" = "#C3C3C3", 
                                 "Protein" = "dodgerblue2", "PTM - normalised" = "seagreen3", 
                                 "PTM_estimated" = "palevioletred2", "Peptidoform" = "gray36"))  +
  #scale_size_manual(values = c(1, 2)) +
  labs(title = i, x = "BioReplicate", y = "LogIntensity") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 60, hjust=1, size = 16),
        axis.text.y = element_text(size = 16),
        legend.text=element_text(size=10),
        axis.title.y = element_text(size = 22),
        axis.title.x = element_text(size = 22),
        title = element_text(size = 22),
        strip.text = element_text(size = 16),
        legend.title =  element_blank(),
        legend.direction = "horizontal",
        legend.position = c(0.5, 0.05)) +
  annotate("text", x = 2.5, y = 30, label = "CCCP", size = 8) +
  annotate("text", x = 6.5, y = 30, label = "Combo", size = 8) +
  annotate("text", x = 10.5, y = 30, label = "Ctrl", size = 8) +
  annotate("text", x = 14.5, y = 30, label = "USP30", size = 8) +
  ylim(-5, 30)

plots[[i]] <- p1
}
plots
```


```{r, fig.width=8, fig.height=10}
plots <- list()
for (i in ptm_list2){
prot_ = str_split(i, "_")[[1]][1]
site_ = str_split(i, "_")[[1]][2]

pepform_df <- longFormat(pe[,,"pepformRel"], rowvars = c("protein", "ptm"), colvars = c("group")) %>% as.data.frame()
pepform_df <- pepform_df %>% filter(protein == prot_)
pepform_df <- pepform_df %>% filter(grepl(site_, ptm, fixed = T))
pepform_df$FeatureType <- "Peptide"
pepform_df <- pepform_df %>% arrange(group, rowname)

ptm_df <- longFormat(pe[,,"ptmRel"], rowvars = c("protein", "ptm"), colvars = c("group")) %>% as.data.frame()
ptm_df <- ptm_df %>% filter(ptm == i)
ptm_df$FeatureType <- "PTM"
ptm_df <- ptm_df %>% arrange(group, rowname)

plot_df <- rbindlist(list(pepform_df, ptm_df), fill = TRUE)

plot_df[plot_df$FeatureType == 'PTM'][['FeatureType']] <- "PTM Summarized"
plot_df[plot_df$FeatureType == 'Peptide'][['FeatureType']] <- "PTM Feature"
plot_df$primary <- forcats::fct_inorder(plot_df$primary)

p1 <- plot_df %>% ggplot() +
  geom_line(aes(x = primary, y = value , group = rowname, color = FeatureType), size =2) +
  geom_point(aes(x = primary, y = value , group = rowname, color = FeatureType), size = 5) +
  geom_vline(data=data.frame(x = c(4.5, 8.5, 12.5)),
             aes(xintercept=as.numeric(x)), linetype = "dashed") +
  scale_colour_manual(values = c("#C3C3C3", "#D55E00")) +
  #scale_size_manual(values = c(1, 2)) +
  labs(title = i, x = "BioReplicate", y = "Abundance") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 60, hjust=1, size = 16),
        axis.text.y = element_text(size = 16),
        legend.text=element_text(size=10),
        axis.title.y = element_text(size = 22),
        axis.title.x = element_text(size = 22),
        title = element_text(size = 22),
        strip.text = element_text(size = 16),
        legend.title =  element_blank(),
        legend.direction = "horizontal",
        legend.position = c(0.5, 0)) +
  annotate("text", x = 2.5, y = 6.5, label = "Ctrl", size = 6) +
  annotate("text", x = 6.5, y = 6.5, label = "CCCP", size = 6) +
  annotate("text", x = 10.5, y = 6.5, label = "Combo", size = 6) +
  annotate("text", x = 14.5, y = 6.5, label = "USP30", size = 6) +
  ylim(-6, 6.5)

plots[[i]] <- p1
}
plots
```

### For MSstats significant ptms

Plots of MSstats first 40 significant PTMs


```{r, fig.width=8, fig.height=10}
## Plot fourty ptm's from Combo vs Ctrl
plots <- c()
for (i in ptm_list1){
  print(i)
  if(!i %in% rownames(assay(pe[["ptmRel"]]))){
    print(paste(i, "not in msqrob"))
    next
    }
  if(i %in% ptm_list2){print(paste(i, "also significant with msqrob"))}
  prot <- strsplit(i, "_")[[1]][1]

#Protein abundance
protein <- assay(pe[["proteinRaw"]])[prot,] %>% as.data.frame() 
colnames(protein) <- "LogIntensities"
protein <- protein %>%
  mutate(Protein = prot,
         FeatureType = "Protein",
         GROUP = sapply(strsplit(rownames(protein), "-"), function(x) x[1])) %>%
         rownames_to_column("originalRUN") %>%
         arrange(GROUP)

#Peptidoform abundance
pepforms <- rowData(pe[["pepformRel"]]) %>% as.data.frame() %>% 
    filter(ptm == i) %>% rownames
pepform <- assay(pe[["pepformRel"]])[pepforms,] %>% as.data.frame()
if((nrow(rowData(pe[["peptidoformRaw"]]) %>% as.data.frame() %>% 
         filter(ptm == i))==0)){next}
if (length(pepforms)==0){
  print(paste(i, "not in normalised PTM data"))
  pepforms <- rowData(pe[["peptidoformRaw"]]) %>% as.data.frame() %>% 
    filter(ptm == i) %>% rownames
  pepform <- assay(pe[["peptidoformRaw"]])[pepforms,] %>% as.data.frame()
  if (length(pepforms)>1){
  pepform <- pepform %>% rownames_to_column("Protein") %>% 
    pivot_longer(cols = colnames(pepform), names_to = "originalRUN",
                 values_to = "LogIntensities") 
  pepform$GROUP <- sapply(strsplit(pepform$originalRUN, "-"), function(x) x[1])
  pepform <- pepform %>% arrange(pepform, GROUP)
} else {colnames(pepform) <- "LogIntensities"
        pepform <- pepform %>% rownames_to_column("originalRUN") 
        pepform <- pepform %>% mutate(GROUP = 
                    sapply(strsplit(pepform$originalRUN, "-"), function(x) x[1]),
                    Protein = pepforms[1]) %>%
                   arrange(GROUP)
        }
  pepform <- pepform %>%
  mutate(FeatureType = "pepform - unnormalised")
  plot_df <- rbindlist(list(protein, pepform), fill = TRUE)
  p1 <- plot_df %>% ggplot() +
  geom_line(aes(x = originalRUN, y = LogIntensities , group = Protein, color = FeatureType), size =2) +
  geom_point(aes(x = originalRUN, y = LogIntensities , group = Protein, color = FeatureType), size = 5) +
  geom_vline(data=data.frame(x = c(4.5, 8.5, 12.5)),
             aes(xintercept=as.numeric(x)), linetype = "dashed") +
  scale_colour_manual(values = c("pepform - unnormalised" = "lightgoldenrod", "Protein" = "palegreen2", 
                                 "PTM" = "palevioletred2", "PTM_estimated" = "deeppink4")) +
  #scale_size_manual(values = c(1, 2)) +
  labs(title = paste(i, "no normalisation possible"), x = "BioReplicate", y = "LogIntensity") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 60, hjust=1, size = 16),
        axis.text.y = element_text(size = 16),
        legend.text=element_text(size=10),
        axis.title.y = element_text(size = 22),
        axis.title.x = element_text(size = 22),
        title = element_text(size = 22),
        strip.text = element_text(size = 16),
        legend.title =  element_blank(),
        legend.direction = "horizontal",
        legend.position = c(0.5, 0.05)) +
  annotate("text", x = 2.5, y = 27, label = "CCCP", size = 8) +
  annotate("text", x = 6.5, y = 27, label = "Combo", size = 8) +
  annotate("text", x = 10.5, y = 27, label = "Ctrl", size = 8) +
  annotate("text", x = 14.5, y = 27, label = "USP30", size = 8) +
  ylim(-7, 27)

plots[[i]] <- p1
next
}
if (length(pepforms)>1){
  pepform <- pepform %>% rownames_to_column("Protein") %>% 
    pivot_longer(cols = colnames(pepform), names_to = "originalRUN",
                 values_to = "LogIntensities") 
  pepform$GROUP <- sapply(strsplit(pepform$originalRUN, "-"), function(x) x[1])
  pepform <- pepform %>% arrange(pepform, GROUP) %>%
    mutate(Protein = paste(Protein, "normalised"))
} else {colnames(pepform) <- "LogIntensities"
        pepform <- pepform %>% rownames_to_column("originalRUN") 
        pepform <- pepform %>% mutate(GROUP = 
                    sapply(strsplit(pepform$originalRUN, "-"), function(x) x[1]),
                    Protein = paste(pepforms[1], "normalised")) %>%
                   arrange(GROUP)
        }
pepform <- pepform %>%
  mutate(FeatureType = "Peptidoform - Normalised")

pepform$originalRUN <- forcats::fct_inorder(pepform$originalRUN)

pepforms_raw <- rowData(pe[["peptidoformRaw"]]) %>% as.data.frame() %>% 
    filter(ptm == i) %>% rownames
pepform_raw <- assay(pe[["peptidoformRaw"]])[pepforms_raw,] %>% as.data.frame()
if (length(pepforms_raw)>1){
  pepform_raw <- pepform_raw %>% rownames_to_column("Protein") %>% 
    pivot_longer(cols = colnames(pepform_raw), names_to = "originalRUN",
                 values_to = "LogIntensities") 
  pepform_raw$GROUP <- sapply(strsplit(pepform_raw$originalRUN, "-"), function(x) x[1])
  pepform_raw <- pepform_raw %>% arrange(pepform_raw, GROUP)
} else {colnames(pepform_raw) <- "LogIntensities"
        pepform_raw <- pepform_raw %>% rownames_to_column("originalRUN") 
        pepform_raw <- pepform_raw %>% mutate(GROUP = 
                    sapply(strsplit(pepform_raw$originalRUN, "-"), function(x) x[1]),
                    Protein = pepforms_raw[1]) %>%
                   arrange(GROUP)
        }
pepform_raw <- pepform_raw %>%
  mutate(FeatureType = "Peptidoform")


#Normalised PTM abundance
ptm_df <- assay(pe[["ptmRel"]])[i,] %>% as.data.frame()
colnames(ptm_df) <- "LogIntensities"
ptm_df <- ptm_df %>%
  mutate(Protein = i,
         FeatureType = "PTM - normalised",
         GROUP = sapply(strsplit(rownames(ptm_df), "-"), function(x) x[1])) %>%
         rownames_to_column("originalRUN") %>%
         arrange(GROUP)


#PTM estimated
Protein <- paste(rep(i, nrow(ptm_df)), "estimate")
ptm_estimate <- tibble(Protein)
ptm_estimate$originalRUN <- ptm_df$originalRUN
ptm_estimate$GROUP <- ptm_df$GROUP
ptm_estimate$FeatureType <- "PTM_estimated"
ptm_estimate$LogIntensities <- NA

fixeffects <- rowData(pe[["ptmRel"]])$msqrobModels[[i]] %>% getCoef
if (! all(is.finite(fixeffects))){print(paste(i, "no refitting possible"))
  next}
ptm_estimate[ptm_estimate$GROUP=="Ctrl",]$LogIntensities <- 
                                                    fixeffects[["(Intercept)"]]
ptm_estimate[ptm_estimate$GROUP=="Combo",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("Combo", names(fixeffects))][1]
ptm_estimate[ptm_estimate$GROUP=="CCCP",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("CCCP", names(fixeffects))][1]
ptm_estimate[ptm_estimate$GROUP=="USP30_OE",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("USP30_OE", names(fixeffects))][1]


plot_df <- rbindlist(list(protein, pepform, ptm_df, ptm_estimate, pepform_raw), fill = TRUE)
plot_df$originalRUN <- as.vector(plot_df$originalRUN)
plot_df <- plot_df %>% arrange(FeatureType, originalRUN)
plot_df$originalRUN <- forcats::fct_inorder(plot_df$originalRUN)
plot_points <- plot_df %>% filter(FeatureType != "PTM_estimated")

#plot_df[plot_df$FeatureType == 'Model'][['FeatureType']] <- "PTM Summarized"
#plot_df[plot_df$FeatureType == 'Peptide'][['FeatureType']] <- "PTM Feature"

p1 <- plot_df %>% ggplot() +
  geom_line(aes(x = originalRUN, y = LogIntensities , group = Protein, color = FeatureType), size =2) +
  geom_point(data = plot_points, aes(x = originalRUN, y = LogIntensities , group = Protein, color = FeatureType), size = 5) +
  geom_vline(data=data.frame(x = c(4.5, 8.5, 12.5)),
             aes(xintercept=as.numeric(x)), linetype = "dashed") +
  scale_colour_manual(values = c("Peptidoform - normalised" = "#C3C3C3", 
                                 "Protein" = "palegreen2", "PTM - normalised" = "goldenrod2", 
                                 "PTM_estimated" = "deeppink4", "Peptidoform" = "gray36"))+
  #scale_size_manual(values = c(1, 2)) +
  labs(title = i, x = "BioReplicate", y = "LogIntensity") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 60, hjust=1, size = 16),
        axis.text.y = element_text(size = 16),
        legend.text=element_text(size=11.3),
        axis.title.y = element_text(size = 22),
        axis.title.x = element_text(size = 22),
        title = element_text(size = 22),
        strip.text = element_text(size = 16),
        legend.title =  element_blank(),
        legend.direction = "horizontal",
        legend.position = c(0.5, 0.05)) +
  annotate("text", x = 2.5, y = 30, label = "CCCP", size = 8) +
  annotate("text", x = 6.5, y = 30, label = "Combo", size = 8) +
  annotate("text", x = 10.5, y = 30, label = "Ctrl", size = 8) +
  annotate("text", x = 14.5, y = 30, label = "USP30", size = 8) +
  ylim(-5, 30)

plots[[i]] <- p1
}
plots
```

```{r}
for (i in ptm_list1){
  print(i)
  if(!i %in% rownames(assay(pe[["ptmRel"]]))){
    print(paste(i, "not in msqrob"))
    next
    }
  print(rowData(pe[["ptmRel"]])$groupCombo[i,])
  }
```


## Msqrob for MSstats summary

estimated ptm abundance -> rowData(peMSstatsSum[["ptmNorm"]])$msqrobModels$P18124_K029 %>% getCoef()
protein abundance -> summarized_ptm$PROTEIN$ProteinLevelData %>% filter(Protein == i) %>% pull(LogIntensities)
ptm abundance -> assay(peMSstatsSum[["ptm]]) of summarized_ptm$PTM$ProteinLevelData %>% filter(Protein == i) %>% pull(LogIntensities)
normalised ptm abundance -> assay(peMSstatsSum[["ptmNorm]])

```{r, fig.width=8, fig.height=10}
## Plot significant ptm's from Combo vs Ctrl
ptm_list3 <- rowData(pe[["ptmMSstatsRel"]])$groupCombo %>% filter(adjPval <= 0.05) %>% rownames()
plots <- c()
for (i in ptm_list3){
  prot <- strsplit(i, "_")[[1]][1]

#Protein abundance
protein <- tibble(originalRUN, GROUP)
protein$FeatureType <- "Protein"
protein$Protein <- prot
tmp <- summarized_ptm$PROTEIN$ProteinLevelData %>% filter(Protein == prot) %>%
  select(c(LogIntensities, originalRUN))
missing_runs <- setdiff(originalRUN, tmp$originalRUN)
tmp2 <- tibble(originalRUN = missing_runs, LogIntensities = NA)
tmp3 <- rbind(tmp, tmp2) %>% arrange(originalRUN) %>% select(LogIntensities)
protein <- cbind(protein, tmp3)

#PTM abundance
ptm_df <- tibble(originalRUN, GROUP)
ptm_df$FeatureType <- "PTM"
ptm_df$Protein <- i
tmp <- summarized_ptm$PTM$ProteinLevelData %>% filter(Protein == i) %>% 
  select(c(LogIntensities, originalRUN))
missing_runs <- setdiff(originalRUN, tmp$originalRUN)
tmp2 <- tibble(originalRUN = missing_runs, LogIntensities = NA)
tmp3 <- rbind(tmp, tmp2) %>% arrange(originalRUN) %>% select(LogIntensities)
ptm_df <- cbind(ptm_df, tmp3)


#Normalised PTM abundance
ptm_df_norm <- assay(pe[["ptmMSstatsRel"]])[i,] %>% as.data.frame()
colnames(ptm_df_norm) <- "LogIntensities"
ptm_df_norm <- ptm_df_norm %>%
  mutate(Protein = i,
         FeatureType = "PTM - normalised",
         GROUP = sapply(strsplit(rownames(ptm_df_norm), "-"), function(x) x[1])) %>%
         rownames_to_column("originalRUN") %>%
         arrange(GROUP)


#PTM estimated
Protein <- rep(i, nrow(ptm_df))
ptm_estimate <- tibble(Protein)
ptm_estimate$originalRUN <- ptm_df$originalRUN
ptm_estimate$GROUP <- ptm_df$GROUP
ptm_estimate$FeatureType <- "PTM_estimated"
ptm_estimate$LogIntensities <- NA

fixeffects <- rowData(pe[["ptmMSstatsRel"]])$msqrobModels[[i]] %>% getCoef
ptm_estimate[ptm_estimate$GROUP=="Ctrl",]$LogIntensities <- 
                                                    fixeffects[["(Intercept)"]]
ptm_estimate[ptm_estimate$GROUP=="Combo",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("Combo", names(fixeffects))][1]
ptm_estimate[ptm_estimate$GROUP=="CCCP",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("CCCP", names(fixeffects))][1]
ptm_estimate[ptm_estimate$GROUP=="USP30_OE",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("USP30_OE", names(fixeffects))][1]


plot_df <- rbindlist(list(protein, ptm_df_norm, ptm_df, ptm_estimate), fill = TRUE)
plot_df <- plot_df %>% arrange(FeatureType)
plot_df$originalRUN <- forcats::fct_inorder(plot_df$originalRUN)
plot_points <- plot_df %>% filter(FeatureType != "PTM_estimated")

#plot_df[plot_df$FeatureType == 'Model'][['FeatureType']] <- "PTM Summarized"
#plot_df[plot_df$FeatureType == 'Peptide'][['FeatureType']] <- "PTM Feature"

p1 <- plot_df %>% ggplot() +
  geom_line(aes(x = originalRUN, y = LogIntensities , group = FeatureType, color = FeatureType), size =2) +
  geom_point(data = plot_points, aes(x = originalRUN, y = LogIntensities , group = FeatureType, color = FeatureType), size = 5) +
  geom_vline(data=data.frame(x = c(4.5, 8.5, 12.5)),
             aes(xintercept=as.numeric(x)), linetype = "dashed") +
    scale_colour_manual(values = c("PTM - normalised" = "lightgoldenrod", "Protein" = "skyblue3", 
                                 "PTM" = "seagreen3", "PTM_estimated" = "palevioletred2")) +
  #scale_size_manual(values = c(1, 2)) +
  labs(title = i, x = "BioReplicate", y = "LogIntensity") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 60, hjust=1, size = 16),
        axis.text.y = element_text(size = 16),
        legend.text=element_text(size=10),
        axis.title.y = element_text(size = 22),
        axis.title.x = element_text(size = 22),
        title = element_text(size = 22),
        strip.text = element_text(size = 16),
        legend.title =  element_blank(),
        legend.direction = "horizontal",
        legend.position = c(0.5, 0)) +
  annotate("text", x = 2.5, y = 27, label = "CCCP", size = 8) +
  annotate("text", x = 6.5, y = 27, label = "Combo", size = 8) +
  annotate("text", x = 10.5, y = 27, label = "Ctrl", size = 8) +
  annotate("text", x = 14.5, y = 27, label = "USP30", size = 8) +
  ylim(-7, 27)

plots[[i]] <- p1
}
plots
```

### For MSstats significant ptms

```{r, fig.width=8, fig.height=10}
## Plot fourty ptm's from Combo vs Ctrl
originalRUN <- c("CCCP-B1T1", "CCCP-B1T2", "CCCP-B2T1", "CCCP-B2T2", "Combo-B1T1", 
                 "Combo-B1T2", "Combo-B2T1", "Combo-B2T2", "Ctrl-B1T1", "Ctrl-B1T2",   
                 "Ctrl-B2T1", "Ctrl-B2T2", "USP30_OE-B1T1", "USP30_OE-B1T2", 
                 "USP30_OE-B2T1", "USP30_OE-B2T2")
GROUP <- sapply(strsplit(originalRUN, "-"), function(x) x[1])
plots <- c()
for (i in ptm_list1){
  if (i %in% ptm_list3){print(paste(i, "also significant in msqrob"))}
  prot <- strsplit(i, "_")[[1]][1]

#Protein abundance
protein <- tibble(originalRUN, GROUP)
protein$FeatureType <- "Protein"
protein$Protein <- prot
tmp <- summarized_ptm$PROTEIN$ProteinLevelData %>% filter(Protein == prot) %>%
  select(c(LogIntensities, originalRUN))
missing_runs <- setdiff(originalRUN, tmp$originalRUN)
tmp2 <- tibble(originalRUN = missing_runs, LogIntensities = NA)
tmp3 <- rbind(tmp, tmp2) %>% arrange(originalRUN) %>% select(LogIntensities)
protein <- cbind(protein, tmp3)


#PTM abundance
ptm_df <- tibble(originalRUN, GROUP)
ptm_df$FeatureType <- "PTM"
ptm_df$Protein <- i
tmp <- summarized_ptm$PTM$ProteinLevelData %>% filter(Protein == i) %>% 
  select(c(LogIntensities, originalRUN))
missing_runs <- setdiff(originalRUN, tmp$originalRUN)
tmp2 <- tibble(originalRUN = missing_runs, LogIntensities = NA)
tmp3 <- rbind(tmp, tmp2) %>% arrange(originalRUN) %>% select(LogIntensities)
ptm_df <- cbind(ptm_df, tmp3)

#Normalised PTM abundance
if (!i %in% rownames(assay(pe[["ptmMSstatsRel"]]))){
  print(paste(i, "not in normalised PTM data"))
  plot_df <- rbindlist(list(protein, ptm_df), fill = TRUE)
  p1 <- plot_df %>% ggplot() +
  geom_line(aes(x = originalRUN, y = LogIntensities , group = FeatureType, color = FeatureType), size =2) +
  geom_point(aes(x = originalRUN, y = LogIntensities , group = FeatureType, color = FeatureType), size = 5) +
  geom_vline(data=data.frame(x = c(4.5, 8.5, 12.5)),
             aes(xintercept=as.numeric(x)), linetype = "dashed") +
  scale_colour_manual(values = c("PTM - normalised" = "lightgoldenrod", "Protein" = "skyblue3", 
                                 "PTM" = "seagreen3", "PTM_estimated" = "palevioletred2")) +
  #scale_size_manual(values = c(1, 2)) +
  labs(title = paste(i, "no normalisation possible"), x = "BioReplicate", y = "LogIntensity") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 60, hjust=1, size = 16),
        axis.text.y = element_text(size = 16),
        legend.text=element_text(size=10),
        axis.title.y = element_text(size = 22),
        axis.title.x = element_text(size = 22),
        title = element_text(size = 22),
        strip.text = element_text(size = 16),
        legend.title =  element_blank(),
        legend.direction = "horizontal",
        legend.position = c(0.5, 0)) +
  annotate("text", x = 2.5, y = 27, label = "CCCP", size = 8) +
  annotate("text", x = 6.5, y = 27, label = "Combo", size = 8) +
  annotate("text", x = 10.5, y = 27, label = "Ctrl", size = 8) +
  annotate("text", x = 14.5, y = 27, label = "USP30", size = 8) +
  ylim(-7, 27)

plots[[i]] <- p1
next
}
ptm_df_norm <- assay(pe[["ptmMSstatsRel"]])[i,] %>% as.data.frame()
colnames(ptm_df_norm) <- "LogIntensities"
ptm_df_norm <- ptm_df_norm %>%
  mutate(Protein = i,
         FeatureType = "PTM - normalised",
         GROUP = sapply(strsplit(rownames(ptm_df_norm), "-"), function(x) x[1])) %>%
         rownames_to_column("originalRUN") %>%
         arrange(GROUP)


#PTM estimated
Protein <- rep(i, nrow(ptm_df))
ptm_estimate <- tibble(Protein)
ptm_estimate$originalRUN <- ptm_df$originalRUN
ptm_estimate$GROUP <- ptm_df$GROUP
ptm_estimate$FeatureType <- "PTM_estimated"
ptm_estimate$LogIntensities <- NA

if(rowData(pe[["ptmMSstatsRel"]])$msqrobModels[[i]] %>% getFitMethod() == "fitError"){
    print(paste(i, "resulted in a fitError"))
  plot_df <- rbindlist(list(protein, ptm_df, ptm_df_norm), fill = TRUE)
  p1 <- plot_df %>% ggplot() +
  geom_line(aes(x = originalRUN, y = LogIntensities , group = FeatureType, color = FeatureType), size =2) +
  geom_point(aes(x = originalRUN, y = LogIntensities , group = FeatureType, color = FeatureType), size = 5) +
  geom_vline(data=data.frame(x = c(4.5, 8.5, 12.5)),
             aes(xintercept=as.numeric(x)), linetype = "dashed") +
  scale_colour_manual(values = c("PTM - normalised" = "lightgoldenrod", "Protein" = "skyblue3", 
                                 "PTM" = "seagreen3", "PTM_estimated" = "palevioletred2")) +
  #scale_size_manual(values = c(1, 2)) +
  labs(title = paste(i, "fitError"), x = "BioReplicate", y = "LogIntensity") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 60, hjust=1, size = 16),
        axis.text.y = element_text(size = 16),
        legend.text=element_text(size=10),
        axis.title.y = element_text(size = 22),
        axis.title.x = element_text(size = 22),
        title = element_text(size = 22),
        strip.text = element_text(size = 16),
        legend.title =  element_blank(),
        legend.direction = "horizontal",
        legend.position = c(0.5, 0)) +
  annotate("text", x = 2.5, y = 27, label = "CCCP", size = 8) +
  annotate("text", x = 6.5, y = 27, label = "Combo", size = 8) +
  annotate("text", x = 10.5, y = 27, label = "Ctrl", size = 8) +
  annotate("text", x = 14.5, y = 27, label = "USP30", size = 8) +
  ylim(-7, 27)

plots[[i]] <- p1
  next
}
fixeffects <- rowData(pe[["ptmMSstatsRel"]])$msqrobModels[[i]] %>% getCoef
try(ptm_estimate[ptm_estimate$GROUP=="Ctrl",]$LogIntensities <- 
                                                    fixeffects[["(Intercept)"]])
try(ptm_estimate[ptm_estimate$GROUP=="Combo",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("Combo", names(fixeffects))][1])
try(ptm_estimate[ptm_estimate$GROUP=="CCCP",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("CCCP", names(fixeffects))][1])
try(ptm_estimate[ptm_estimate$GROUP=="USP30_OE",]$LogIntensities <- 
   fixeffects[["(Intercept)"]] + fixeffects[grepl("USP30_OE", names(fixeffects))][1])


plot_df <- rbindlist(list(protein, ptm_df_norm, ptm_df, ptm_estimate), fill = TRUE)
plot_df <- plot_df %>% arrange(FeatureType)
plot_df$originalRUN <- forcats::fct_inorder(plot_df$originalRUN)
plot_points <- plot_df %>% filter(FeatureType != "PTM_estimated")

#plot_df[plot_df$FeatureType == 'Model'][['FeatureType']] <- "PTM Summarized"
#plot_df[plot_df$FeatureType == 'Peptide'][['FeatureType']] <- "PTM Feature"

p1 <- plot_df %>% ggplot() +
  geom_line(aes(x = originalRUN, y = LogIntensities , group = FeatureType, color = FeatureType), size =2) +
  geom_point(data = plot_points, aes(x = originalRUN, y = LogIntensities , group = FeatureType, color = FeatureType), size = 5) +
  geom_vline(data=data.frame(x = c(4.5, 8.5, 12.5)),
             aes(xintercept=as.numeric(x)), linetype = "dashed") +
  scale_colour_manual(values = c("PTM - normalised" = "lightgoldenrod", "Protein" = "skyblue3", 
                                 "PTM" = "seagreen3", "PTM_estimated" = "palevioletred2")) +
  #scale_size_manual(values = c(1, 2)) +
  labs(title = i, x = "BioReplicate", y = "LogIntensity") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 60, hjust=1, size = 16),
        axis.text.y = element_text(size = 16),
        legend.text=element_text(size=10),
        axis.title.y = element_text(size = 22),
        axis.title.x = element_text(size = 22),
        title = element_text(size = 22),
        strip.text = element_text(size = 16),
        legend.title =  element_blank(),
        legend.direction = "horizontal",
        legend.position = c(0.5, 0)) +
  annotate("text", x = 2.5, y = 30, label = "CCCP", size = 8) +
  annotate("text", x = 6.5, y = 30, label = "Combo", size = 8) +
  annotate("text", x = 10.5, y = 30, label = "Ctrl", size = 8) +
  annotate("text", x = 14.5, y = 30, label = "USP30", size = 8) +
  ylim(-10, 30)

plots[[i]] <- p1
}
plots
```

```{r}
for (i in ptm_list1){
  print(i)
  print(rowData(pe[["ptmMSstatsRel"]])$groupCombo[i,])
  }
```
